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  6. BatchPredictionJob

Google Cloud Native is in preview. Google Cloud Classic is fully supported.

Google Cloud Native v0.32.0 published on Wednesday, Nov 29, 2023 by Pulumi
pulumi/pulumi-google-native

google-native.aiplatform/v1.BatchPredictionJob

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Google Cloud Native is in preview. Google Cloud Classic is fully supported.

Google Cloud Native v0.32.0 published on Wednesday, Nov 29, 2023 by Pulumi
pulumi/pulumi-google-native

Creates a BatchPredictionJob. A BatchPredictionJob once created will right away be attempted to start. Auto-naming is currently not supported for this resource.

Create BatchPredictionJob Resource

Resources are created with functions called constructors. To learn more about declaring and configuring resources, see Resources.

Constructor syntax

new BatchPredictionJob(name: string, args: BatchPredictionJobArgs, opts?: CustomResourceOptions);
@overload
def BatchPredictionJob(resource_name: str,
                       args: BatchPredictionJobArgs,
                       opts: Optional[ResourceOptions] = None)

@overload
def BatchPredictionJob(resource_name: str,
                       opts: Optional[ResourceOptions] = None,
                       input_config: Optional[GoogleCloudAiplatformV1BatchPredictionJobInputConfigArgs] = None,
                       output_config: Optional[GoogleCloudAiplatformV1BatchPredictionJobOutputConfigArgs] = None,
                       display_name: Optional[str] = None,
                       labels: Optional[Mapping[str, str]] = None,
                       manual_batch_tuning_parameters: Optional[GoogleCloudAiplatformV1ManualBatchTuningParametersArgs] = None,
                       generate_explanation: Optional[bool] = None,
                       encryption_spec: Optional[GoogleCloudAiplatformV1EncryptionSpecArgs] = None,
                       instance_config: Optional[GoogleCloudAiplatformV1BatchPredictionJobInstanceConfigArgs] = None,
                       dedicated_resources: Optional[GoogleCloudAiplatformV1BatchDedicatedResourcesArgs] = None,
                       location: Optional[str] = None,
                       explanation_spec: Optional[GoogleCloudAiplatformV1ExplanationSpecArgs] = None,
                       model: Optional[str] = None,
                       model_parameters: Optional[Any] = None,
                       disable_container_logging: Optional[bool] = None,
                       project: Optional[str] = None,
                       service_account: Optional[str] = None,
                       unmanaged_container_model: Optional[GoogleCloudAiplatformV1UnmanagedContainerModelArgs] = None)
func NewBatchPredictionJob(ctx *Context, name string, args BatchPredictionJobArgs, opts ...ResourceOption) (*BatchPredictionJob, error)
public BatchPredictionJob(string name, BatchPredictionJobArgs args, CustomResourceOptions? opts = null)
public BatchPredictionJob(String name, BatchPredictionJobArgs args)
public BatchPredictionJob(String name, BatchPredictionJobArgs args, CustomResourceOptions options)

type: google-native:aiplatform/v1:BatchPredictionJob
properties: # The arguments to resource properties.
options: # Bag of options to control resource's behavior.

Parameters

name This property is required. string
The unique name of the resource.
args This property is required. BatchPredictionJobArgs
The arguments to resource properties.
opts CustomResourceOptions
Bag of options to control resource's behavior.
resource_name This property is required. str
The unique name of the resource.
args This property is required. BatchPredictionJobArgs
The arguments to resource properties.
opts ResourceOptions
Bag of options to control resource's behavior.
ctx Context
Context object for the current deployment.
name This property is required. string
The unique name of the resource.
args This property is required. BatchPredictionJobArgs
The arguments to resource properties.
opts ResourceOption
Bag of options to control resource's behavior.
name This property is required. string
The unique name of the resource.
args This property is required. BatchPredictionJobArgs
The arguments to resource properties.
opts CustomResourceOptions
Bag of options to control resource's behavior.
name This property is required. String
The unique name of the resource.
args This property is required. BatchPredictionJobArgs
The arguments to resource properties.
options CustomResourceOptions
Bag of options to control resource's behavior.

Constructor example

The following reference example uses placeholder values for all input properties.

var batchPredictionJobResource = new GoogleNative.Aiplatform.V1.BatchPredictionJob("batchPredictionJobResource", new()
{
    InputConfig = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1BatchPredictionJobInputConfigArgs
    {
        InstancesFormat = "string",
        BigquerySource = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1BigQuerySourceArgs
        {
            InputUri = "string",
        },
        GcsSource = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1GcsSourceArgs
        {
            Uris = new[]
            {
                "string",
            },
        },
    },
    OutputConfig = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1BatchPredictionJobOutputConfigArgs
    {
        PredictionsFormat = "string",
        BigqueryDestination = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1BigQueryDestinationArgs
        {
            OutputUri = "string",
        },
        GcsDestination = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1GcsDestinationArgs
        {
            OutputUriPrefix = "string",
        },
    },
    DisplayName = "string",
    Labels = 
    {
        { "string", "string" },
    },
    ManualBatchTuningParameters = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ManualBatchTuningParametersArgs
    {
        BatchSize = 0,
    },
    GenerateExplanation = false,
    EncryptionSpec = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1EncryptionSpecArgs
    {
        KmsKeyName = "string",
    },
    InstanceConfig = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1BatchPredictionJobInstanceConfigArgs
    {
        ExcludedFields = new[]
        {
            "string",
        },
        IncludedFields = new[]
        {
            "string",
        },
        InstanceType = "string",
        KeyField = "string",
    },
    DedicatedResources = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1BatchDedicatedResourcesArgs
    {
        MachineSpec = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1MachineSpecArgs
        {
            AcceleratorCount = 0,
            AcceleratorType = GoogleNative.Aiplatform.V1.GoogleCloudAiplatformV1MachineSpecAcceleratorType.AcceleratorTypeUnspecified,
            MachineType = "string",
            TpuTopology = "string",
        },
        MaxReplicaCount = 0,
        StartingReplicaCount = 0,
    },
    Location = "string",
    ExplanationSpec = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ExplanationSpecArgs
    {
        Parameters = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ExplanationParametersArgs
        {
            Examples = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ExamplesArgs
            {
                ExampleGcsSource = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ExamplesExampleGcsSourceArgs
                {
                    DataFormat = GoogleNative.Aiplatform.V1.GoogleCloudAiplatformV1ExamplesExampleGcsSourceDataFormat.DataFormatUnspecified,
                    GcsSource = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1GcsSourceArgs
                    {
                        Uris = new[]
                        {
                            "string",
                        },
                    },
                },
                NearestNeighborSearchConfig = "any",
                NeighborCount = 0,
                Presets = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1PresetsArgs
                {
                    Modality = GoogleNative.Aiplatform.V1.GoogleCloudAiplatformV1PresetsModality.ModalityUnspecified,
                    Query = GoogleNative.Aiplatform.V1.GoogleCloudAiplatformV1PresetsQuery.Precise,
                },
            },
            IntegratedGradientsAttribution = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1IntegratedGradientsAttributionArgs
            {
                StepCount = 0,
                BlurBaselineConfig = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1BlurBaselineConfigArgs
                {
                    MaxBlurSigma = 0,
                },
                SmoothGradConfig = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1SmoothGradConfigArgs
                {
                    FeatureNoiseSigma = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1FeatureNoiseSigmaArgs
                    {
                        NoiseSigma = new[]
                        {
                            new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeatureArgs
                            {
                                Name = "string",
                                Sigma = 0,
                            },
                        },
                    },
                    NoiseSigma = 0,
                    NoisySampleCount = 0,
                },
            },
            OutputIndices = new[]
            {
                "any",
            },
            SampledShapleyAttribution = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1SampledShapleyAttributionArgs
            {
                PathCount = 0,
            },
            TopK = 0,
            XraiAttribution = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1XraiAttributionArgs
            {
                StepCount = 0,
                BlurBaselineConfig = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1BlurBaselineConfigArgs
                {
                    MaxBlurSigma = 0,
                },
                SmoothGradConfig = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1SmoothGradConfigArgs
                {
                    FeatureNoiseSigma = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1FeatureNoiseSigmaArgs
                    {
                        NoiseSigma = new[]
                        {
                            new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeatureArgs
                            {
                                Name = "string",
                                Sigma = 0,
                            },
                        },
                    },
                    NoiseSigma = 0,
                    NoisySampleCount = 0,
                },
            },
        },
        Metadata = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ExplanationMetadataArgs
        {
            Inputs = 
            {
                { "string", "string" },
            },
            Outputs = 
            {
                { "string", "string" },
            },
            FeatureAttributionsSchemaUri = "string",
            LatentSpaceSource = "string",
        },
    },
    Model = "string",
    ModelParameters = "any",
    DisableContainerLogging = false,
    Project = "string",
    ServiceAccount = "string",
    UnmanagedContainerModel = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1UnmanagedContainerModelArgs
    {
        ArtifactUri = "string",
        ContainerSpec = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ModelContainerSpecArgs
        {
            ImageUri = "string",
            Args = new[]
            {
                "string",
            },
            Command = new[]
            {
                "string",
            },
            DeploymentTimeout = "string",
            Env = new[]
            {
                new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1EnvVarArgs
                {
                    Name = "string",
                    Value = "string",
                },
            },
            HealthProbe = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ProbeArgs
            {
                Exec = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ProbeExecActionArgs
                {
                    Command = new[]
                    {
                        "string",
                    },
                },
                PeriodSeconds = 0,
                TimeoutSeconds = 0,
            },
            HealthRoute = "string",
            Ports = new[]
            {
                new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1PortArgs
                {
                    ContainerPort = 0,
                },
            },
            PredictRoute = "string",
            SharedMemorySizeMb = "string",
            StartupProbe = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ProbeArgs
            {
                Exec = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ProbeExecActionArgs
                {
                    Command = new[]
                    {
                        "string",
                    },
                },
                PeriodSeconds = 0,
                TimeoutSeconds = 0,
            },
        },
        PredictSchemata = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1PredictSchemataArgs
        {
            InstanceSchemaUri = "string",
            ParametersSchemaUri = "string",
            PredictionSchemaUri = "string",
        },
    },
});
Copy
example, err := aiplatform.NewBatchPredictionJob(ctx, "batchPredictionJobResource", &aiplatform.BatchPredictionJobArgs{
	InputConfig: &aiplatform.GoogleCloudAiplatformV1BatchPredictionJobInputConfigArgs{
		InstancesFormat: pulumi.String("string"),
		BigquerySource: &aiplatform.GoogleCloudAiplatformV1BigQuerySourceArgs{
			InputUri: pulumi.String("string"),
		},
		GcsSource: &aiplatform.GoogleCloudAiplatformV1GcsSourceArgs{
			Uris: pulumi.StringArray{
				pulumi.String("string"),
			},
		},
	},
	OutputConfig: &aiplatform.GoogleCloudAiplatformV1BatchPredictionJobOutputConfigArgs{
		PredictionsFormat: pulumi.String("string"),
		BigqueryDestination: &aiplatform.GoogleCloudAiplatformV1BigQueryDestinationArgs{
			OutputUri: pulumi.String("string"),
		},
		GcsDestination: &aiplatform.GoogleCloudAiplatformV1GcsDestinationArgs{
			OutputUriPrefix: pulumi.String("string"),
		},
	},
	DisplayName: pulumi.String("string"),
	Labels: pulumi.StringMap{
		"string": pulumi.String("string"),
	},
	ManualBatchTuningParameters: &aiplatform.GoogleCloudAiplatformV1ManualBatchTuningParametersArgs{
		BatchSize: pulumi.Int(0),
	},
	GenerateExplanation: pulumi.Bool(false),
	EncryptionSpec: &aiplatform.GoogleCloudAiplatformV1EncryptionSpecArgs{
		KmsKeyName: pulumi.String("string"),
	},
	InstanceConfig: &aiplatform.GoogleCloudAiplatformV1BatchPredictionJobInstanceConfigArgs{
		ExcludedFields: pulumi.StringArray{
			pulumi.String("string"),
		},
		IncludedFields: pulumi.StringArray{
			pulumi.String("string"),
		},
		InstanceType: pulumi.String("string"),
		KeyField:     pulumi.String("string"),
	},
	DedicatedResources: &aiplatform.GoogleCloudAiplatformV1BatchDedicatedResourcesArgs{
		MachineSpec: &aiplatform.GoogleCloudAiplatformV1MachineSpecArgs{
			AcceleratorCount: pulumi.Int(0),
			AcceleratorType:  aiplatform.GoogleCloudAiplatformV1MachineSpecAcceleratorTypeAcceleratorTypeUnspecified,
			MachineType:      pulumi.String("string"),
			TpuTopology:      pulumi.String("string"),
		},
		MaxReplicaCount:      pulumi.Int(0),
		StartingReplicaCount: pulumi.Int(0),
	},
	Location: pulumi.String("string"),
	ExplanationSpec: &aiplatform.GoogleCloudAiplatformV1ExplanationSpecArgs{
		Parameters: &aiplatform.GoogleCloudAiplatformV1ExplanationParametersArgs{
			Examples: &aiplatform.GoogleCloudAiplatformV1ExamplesArgs{
				ExampleGcsSource: &aiplatform.GoogleCloudAiplatformV1ExamplesExampleGcsSourceArgs{
					DataFormat: aiplatform.GoogleCloudAiplatformV1ExamplesExampleGcsSourceDataFormatDataFormatUnspecified,
					GcsSource: &aiplatform.GoogleCloudAiplatformV1GcsSourceArgs{
						Uris: pulumi.StringArray{
							pulumi.String("string"),
						},
					},
				},
				NearestNeighborSearchConfig: pulumi.Any("any"),
				NeighborCount:               pulumi.Int(0),
				Presets: &aiplatform.GoogleCloudAiplatformV1PresetsArgs{
					Modality: aiplatform.GoogleCloudAiplatformV1PresetsModalityModalityUnspecified,
					Query:    aiplatform.GoogleCloudAiplatformV1PresetsQueryPrecise,
				},
			},
			IntegratedGradientsAttribution: &aiplatform.GoogleCloudAiplatformV1IntegratedGradientsAttributionArgs{
				StepCount: pulumi.Int(0),
				BlurBaselineConfig: &aiplatform.GoogleCloudAiplatformV1BlurBaselineConfigArgs{
					MaxBlurSigma: pulumi.Float64(0),
				},
				SmoothGradConfig: &aiplatform.GoogleCloudAiplatformV1SmoothGradConfigArgs{
					FeatureNoiseSigma: &aiplatform.GoogleCloudAiplatformV1FeatureNoiseSigmaArgs{
						NoiseSigma: aiplatform.GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeatureArray{
							&aiplatform.GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeatureArgs{
								Name:  pulumi.String("string"),
								Sigma: pulumi.Float64(0),
							},
						},
					},
					NoiseSigma:       pulumi.Float64(0),
					NoisySampleCount: pulumi.Int(0),
				},
			},
			OutputIndices: pulumi.Array{
				pulumi.Any("any"),
			},
			SampledShapleyAttribution: &aiplatform.GoogleCloudAiplatformV1SampledShapleyAttributionArgs{
				PathCount: pulumi.Int(0),
			},
			TopK: pulumi.Int(0),
			XraiAttribution: &aiplatform.GoogleCloudAiplatformV1XraiAttributionArgs{
				StepCount: pulumi.Int(0),
				BlurBaselineConfig: &aiplatform.GoogleCloudAiplatformV1BlurBaselineConfigArgs{
					MaxBlurSigma: pulumi.Float64(0),
				},
				SmoothGradConfig: &aiplatform.GoogleCloudAiplatformV1SmoothGradConfigArgs{
					FeatureNoiseSigma: &aiplatform.GoogleCloudAiplatformV1FeatureNoiseSigmaArgs{
						NoiseSigma: aiplatform.GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeatureArray{
							&aiplatform.GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeatureArgs{
								Name:  pulumi.String("string"),
								Sigma: pulumi.Float64(0),
							},
						},
					},
					NoiseSigma:       pulumi.Float64(0),
					NoisySampleCount: pulumi.Int(0),
				},
			},
		},
		Metadata: &aiplatform.GoogleCloudAiplatformV1ExplanationMetadataArgs{
			Inputs: pulumi.StringMap{
				"string": pulumi.String("string"),
			},
			Outputs: pulumi.StringMap{
				"string": pulumi.String("string"),
			},
			FeatureAttributionsSchemaUri: pulumi.String("string"),
			LatentSpaceSource:            pulumi.String("string"),
		},
	},
	Model:                   pulumi.String("string"),
	ModelParameters:         pulumi.Any("any"),
	DisableContainerLogging: pulumi.Bool(false),
	Project:                 pulumi.String("string"),
	ServiceAccount:          pulumi.String("string"),
	UnmanagedContainerModel: &aiplatform.GoogleCloudAiplatformV1UnmanagedContainerModelArgs{
		ArtifactUri: pulumi.String("string"),
		ContainerSpec: &aiplatform.GoogleCloudAiplatformV1ModelContainerSpecArgs{
			ImageUri: pulumi.String("string"),
			Args: pulumi.StringArray{
				pulumi.String("string"),
			},
			Command: pulumi.StringArray{
				pulumi.String("string"),
			},
			DeploymentTimeout: pulumi.String("string"),
			Env: aiplatform.GoogleCloudAiplatformV1EnvVarArray{
				&aiplatform.GoogleCloudAiplatformV1EnvVarArgs{
					Name:  pulumi.String("string"),
					Value: pulumi.String("string"),
				},
			},
			HealthProbe: &aiplatform.GoogleCloudAiplatformV1ProbeArgs{
				Exec: &aiplatform.GoogleCloudAiplatformV1ProbeExecActionArgs{
					Command: pulumi.StringArray{
						pulumi.String("string"),
					},
				},
				PeriodSeconds:  pulumi.Int(0),
				TimeoutSeconds: pulumi.Int(0),
			},
			HealthRoute: pulumi.String("string"),
			Ports: aiplatform.GoogleCloudAiplatformV1PortArray{
				&aiplatform.GoogleCloudAiplatformV1PortArgs{
					ContainerPort: pulumi.Int(0),
				},
			},
			PredictRoute:       pulumi.String("string"),
			SharedMemorySizeMb: pulumi.String("string"),
			StartupProbe: &aiplatform.GoogleCloudAiplatformV1ProbeArgs{
				Exec: &aiplatform.GoogleCloudAiplatformV1ProbeExecActionArgs{
					Command: pulumi.StringArray{
						pulumi.String("string"),
					},
				},
				PeriodSeconds:  pulumi.Int(0),
				TimeoutSeconds: pulumi.Int(0),
			},
		},
		PredictSchemata: &aiplatform.GoogleCloudAiplatformV1PredictSchemataArgs{
			InstanceSchemaUri:   pulumi.String("string"),
			ParametersSchemaUri: pulumi.String("string"),
			PredictionSchemaUri: pulumi.String("string"),
		},
	},
})
Copy
var batchPredictionJobResource = new BatchPredictionJob("batchPredictionJobResource", BatchPredictionJobArgs.builder()
    .inputConfig(GoogleCloudAiplatformV1BatchPredictionJobInputConfigArgs.builder()
        .instancesFormat("string")
        .bigquerySource(GoogleCloudAiplatformV1BigQuerySourceArgs.builder()
            .inputUri("string")
            .build())
        .gcsSource(GoogleCloudAiplatformV1GcsSourceArgs.builder()
            .uris("string")
            .build())
        .build())
    .outputConfig(GoogleCloudAiplatformV1BatchPredictionJobOutputConfigArgs.builder()
        .predictionsFormat("string")
        .bigqueryDestination(GoogleCloudAiplatformV1BigQueryDestinationArgs.builder()
            .outputUri("string")
            .build())
        .gcsDestination(GoogleCloudAiplatformV1GcsDestinationArgs.builder()
            .outputUriPrefix("string")
            .build())
        .build())
    .displayName("string")
    .labels(Map.of("string", "string"))
    .manualBatchTuningParameters(GoogleCloudAiplatformV1ManualBatchTuningParametersArgs.builder()
        .batchSize(0)
        .build())
    .generateExplanation(false)
    .encryptionSpec(GoogleCloudAiplatformV1EncryptionSpecArgs.builder()
        .kmsKeyName("string")
        .build())
    .instanceConfig(GoogleCloudAiplatformV1BatchPredictionJobInstanceConfigArgs.builder()
        .excludedFields("string")
        .includedFields("string")
        .instanceType("string")
        .keyField("string")
        .build())
    .dedicatedResources(GoogleCloudAiplatformV1BatchDedicatedResourcesArgs.builder()
        .machineSpec(GoogleCloudAiplatformV1MachineSpecArgs.builder()
            .acceleratorCount(0)
            .acceleratorType("ACCELERATOR_TYPE_UNSPECIFIED")
            .machineType("string")
            .tpuTopology("string")
            .build())
        .maxReplicaCount(0)
        .startingReplicaCount(0)
        .build())
    .location("string")
    .explanationSpec(GoogleCloudAiplatformV1ExplanationSpecArgs.builder()
        .parameters(GoogleCloudAiplatformV1ExplanationParametersArgs.builder()
            .examples(GoogleCloudAiplatformV1ExamplesArgs.builder()
                .exampleGcsSource(GoogleCloudAiplatformV1ExamplesExampleGcsSourceArgs.builder()
                    .dataFormat("DATA_FORMAT_UNSPECIFIED")
                    .gcsSource(GoogleCloudAiplatformV1GcsSourceArgs.builder()
                        .uris("string")
                        .build())
                    .build())
                .nearestNeighborSearchConfig("any")
                .neighborCount(0)
                .presets(GoogleCloudAiplatformV1PresetsArgs.builder()
                    .modality("MODALITY_UNSPECIFIED")
                    .query("PRECISE")
                    .build())
                .build())
            .integratedGradientsAttribution(GoogleCloudAiplatformV1IntegratedGradientsAttributionArgs.builder()
                .stepCount(0)
                .blurBaselineConfig(GoogleCloudAiplatformV1BlurBaselineConfigArgs.builder()
                    .maxBlurSigma(0)
                    .build())
                .smoothGradConfig(GoogleCloudAiplatformV1SmoothGradConfigArgs.builder()
                    .featureNoiseSigma(GoogleCloudAiplatformV1FeatureNoiseSigmaArgs.builder()
                        .noiseSigma(GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeatureArgs.builder()
                            .name("string")
                            .sigma(0)
                            .build())
                        .build())
                    .noiseSigma(0)
                    .noisySampleCount(0)
                    .build())
                .build())
            .outputIndices("any")
            .sampledShapleyAttribution(GoogleCloudAiplatformV1SampledShapleyAttributionArgs.builder()
                .pathCount(0)
                .build())
            .topK(0)
            .xraiAttribution(GoogleCloudAiplatformV1XraiAttributionArgs.builder()
                .stepCount(0)
                .blurBaselineConfig(GoogleCloudAiplatformV1BlurBaselineConfigArgs.builder()
                    .maxBlurSigma(0)
                    .build())
                .smoothGradConfig(GoogleCloudAiplatformV1SmoothGradConfigArgs.builder()
                    .featureNoiseSigma(GoogleCloudAiplatformV1FeatureNoiseSigmaArgs.builder()
                        .noiseSigma(GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeatureArgs.builder()
                            .name("string")
                            .sigma(0)
                            .build())
                        .build())
                    .noiseSigma(0)
                    .noisySampleCount(0)
                    .build())
                .build())
            .build())
        .metadata(GoogleCloudAiplatformV1ExplanationMetadataArgs.builder()
            .inputs(Map.of("string", "string"))
            .outputs(Map.of("string", "string"))
            .featureAttributionsSchemaUri("string")
            .latentSpaceSource("string")
            .build())
        .build())
    .model("string")
    .modelParameters("any")
    .disableContainerLogging(false)
    .project("string")
    .serviceAccount("string")
    .unmanagedContainerModel(GoogleCloudAiplatformV1UnmanagedContainerModelArgs.builder()
        .artifactUri("string")
        .containerSpec(GoogleCloudAiplatformV1ModelContainerSpecArgs.builder()
            .imageUri("string")
            .args("string")
            .command("string")
            .deploymentTimeout("string")
            .env(GoogleCloudAiplatformV1EnvVarArgs.builder()
                .name("string")
                .value("string")
                .build())
            .healthProbe(GoogleCloudAiplatformV1ProbeArgs.builder()
                .exec(GoogleCloudAiplatformV1ProbeExecActionArgs.builder()
                    .command("string")
                    .build())
                .periodSeconds(0)
                .timeoutSeconds(0)
                .build())
            .healthRoute("string")
            .ports(GoogleCloudAiplatformV1PortArgs.builder()
                .containerPort(0)
                .build())
            .predictRoute("string")
            .sharedMemorySizeMb("string")
            .startupProbe(GoogleCloudAiplatformV1ProbeArgs.builder()
                .exec(GoogleCloudAiplatformV1ProbeExecActionArgs.builder()
                    .command("string")
                    .build())
                .periodSeconds(0)
                .timeoutSeconds(0)
                .build())
            .build())
        .predictSchemata(GoogleCloudAiplatformV1PredictSchemataArgs.builder()
            .instanceSchemaUri("string")
            .parametersSchemaUri("string")
            .predictionSchemaUri("string")
            .build())
        .build())
    .build());
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batch_prediction_job_resource = google_native.aiplatform.v1.BatchPredictionJob("batchPredictionJobResource",
    input_config={
        "instances_format": "string",
        "bigquery_source": {
            "input_uri": "string",
        },
        "gcs_source": {
            "uris": ["string"],
        },
    },
    output_config={
        "predictions_format": "string",
        "bigquery_destination": {
            "output_uri": "string",
        },
        "gcs_destination": {
            "output_uri_prefix": "string",
        },
    },
    display_name="string",
    labels={
        "string": "string",
    },
    manual_batch_tuning_parameters={
        "batch_size": 0,
    },
    generate_explanation=False,
    encryption_spec={
        "kms_key_name": "string",
    },
    instance_config={
        "excluded_fields": ["string"],
        "included_fields": ["string"],
        "instance_type": "string",
        "key_field": "string",
    },
    dedicated_resources={
        "machine_spec": {
            "accelerator_count": 0,
            "accelerator_type": google_native.aiplatform.v1.GoogleCloudAiplatformV1MachineSpecAcceleratorType.ACCELERATOR_TYPE_UNSPECIFIED,
            "machine_type": "string",
            "tpu_topology": "string",
        },
        "max_replica_count": 0,
        "starting_replica_count": 0,
    },
    location="string",
    explanation_spec={
        "parameters": {
            "examples": {
                "example_gcs_source": {
                    "data_format": google_native.aiplatform.v1.GoogleCloudAiplatformV1ExamplesExampleGcsSourceDataFormat.DATA_FORMAT_UNSPECIFIED,
                    "gcs_source": {
                        "uris": ["string"],
                    },
                },
                "nearest_neighbor_search_config": "any",
                "neighbor_count": 0,
                "presets": {
                    "modality": google_native.aiplatform.v1.GoogleCloudAiplatformV1PresetsModality.MODALITY_UNSPECIFIED,
                    "query": google_native.aiplatform.v1.GoogleCloudAiplatformV1PresetsQuery.PRECISE,
                },
            },
            "integrated_gradients_attribution": {
                "step_count": 0,
                "blur_baseline_config": {
                    "max_blur_sigma": 0,
                },
                "smooth_grad_config": {
                    "feature_noise_sigma": {
                        "noise_sigma": [{
                            "name": "string",
                            "sigma": 0,
                        }],
                    },
                    "noise_sigma": 0,
                    "noisy_sample_count": 0,
                },
            },
            "output_indices": ["any"],
            "sampled_shapley_attribution": {
                "path_count": 0,
            },
            "top_k": 0,
            "xrai_attribution": {
                "step_count": 0,
                "blur_baseline_config": {
                    "max_blur_sigma": 0,
                },
                "smooth_grad_config": {
                    "feature_noise_sigma": {
                        "noise_sigma": [{
                            "name": "string",
                            "sigma": 0,
                        }],
                    },
                    "noise_sigma": 0,
                    "noisy_sample_count": 0,
                },
            },
        },
        "metadata": {
            "inputs": {
                "string": "string",
            },
            "outputs": {
                "string": "string",
            },
            "feature_attributions_schema_uri": "string",
            "latent_space_source": "string",
        },
    },
    model="string",
    model_parameters="any",
    disable_container_logging=False,
    project="string",
    service_account="string",
    unmanaged_container_model={
        "artifact_uri": "string",
        "container_spec": {
            "image_uri": "string",
            "args": ["string"],
            "command": ["string"],
            "deployment_timeout": "string",
            "env": [{
                "name": "string",
                "value": "string",
            }],
            "health_probe": {
                "exec_": {
                    "command": ["string"],
                },
                "period_seconds": 0,
                "timeout_seconds": 0,
            },
            "health_route": "string",
            "ports": [{
                "container_port": 0,
            }],
            "predict_route": "string",
            "shared_memory_size_mb": "string",
            "startup_probe": {
                "exec_": {
                    "command": ["string"],
                },
                "period_seconds": 0,
                "timeout_seconds": 0,
            },
        },
        "predict_schemata": {
            "instance_schema_uri": "string",
            "parameters_schema_uri": "string",
            "prediction_schema_uri": "string",
        },
    })
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const batchPredictionJobResource = new google_native.aiplatform.v1.BatchPredictionJob("batchPredictionJobResource", {
    inputConfig: {
        instancesFormat: "string",
        bigquerySource: {
            inputUri: "string",
        },
        gcsSource: {
            uris: ["string"],
        },
    },
    outputConfig: {
        predictionsFormat: "string",
        bigqueryDestination: {
            outputUri: "string",
        },
        gcsDestination: {
            outputUriPrefix: "string",
        },
    },
    displayName: "string",
    labels: {
        string: "string",
    },
    manualBatchTuningParameters: {
        batchSize: 0,
    },
    generateExplanation: false,
    encryptionSpec: {
        kmsKeyName: "string",
    },
    instanceConfig: {
        excludedFields: ["string"],
        includedFields: ["string"],
        instanceType: "string",
        keyField: "string",
    },
    dedicatedResources: {
        machineSpec: {
            acceleratorCount: 0,
            acceleratorType: google_native.aiplatform.v1.GoogleCloudAiplatformV1MachineSpecAcceleratorType.AcceleratorTypeUnspecified,
            machineType: "string",
            tpuTopology: "string",
        },
        maxReplicaCount: 0,
        startingReplicaCount: 0,
    },
    location: "string",
    explanationSpec: {
        parameters: {
            examples: {
                exampleGcsSource: {
                    dataFormat: google_native.aiplatform.v1.GoogleCloudAiplatformV1ExamplesExampleGcsSourceDataFormat.DataFormatUnspecified,
                    gcsSource: {
                        uris: ["string"],
                    },
                },
                nearestNeighborSearchConfig: "any",
                neighborCount: 0,
                presets: {
                    modality: google_native.aiplatform.v1.GoogleCloudAiplatformV1PresetsModality.ModalityUnspecified,
                    query: google_native.aiplatform.v1.GoogleCloudAiplatformV1PresetsQuery.Precise,
                },
            },
            integratedGradientsAttribution: {
                stepCount: 0,
                blurBaselineConfig: {
                    maxBlurSigma: 0,
                },
                smoothGradConfig: {
                    featureNoiseSigma: {
                        noiseSigma: [{
                            name: "string",
                            sigma: 0,
                        }],
                    },
                    noiseSigma: 0,
                    noisySampleCount: 0,
                },
            },
            outputIndices: ["any"],
            sampledShapleyAttribution: {
                pathCount: 0,
            },
            topK: 0,
            xraiAttribution: {
                stepCount: 0,
                blurBaselineConfig: {
                    maxBlurSigma: 0,
                },
                smoothGradConfig: {
                    featureNoiseSigma: {
                        noiseSigma: [{
                            name: "string",
                            sigma: 0,
                        }],
                    },
                    noiseSigma: 0,
                    noisySampleCount: 0,
                },
            },
        },
        metadata: {
            inputs: {
                string: "string",
            },
            outputs: {
                string: "string",
            },
            featureAttributionsSchemaUri: "string",
            latentSpaceSource: "string",
        },
    },
    model: "string",
    modelParameters: "any",
    disableContainerLogging: false,
    project: "string",
    serviceAccount: "string",
    unmanagedContainerModel: {
        artifactUri: "string",
        containerSpec: {
            imageUri: "string",
            args: ["string"],
            command: ["string"],
            deploymentTimeout: "string",
            env: [{
                name: "string",
                value: "string",
            }],
            healthProbe: {
                exec: {
                    command: ["string"],
                },
                periodSeconds: 0,
                timeoutSeconds: 0,
            },
            healthRoute: "string",
            ports: [{
                containerPort: 0,
            }],
            predictRoute: "string",
            sharedMemorySizeMb: "string",
            startupProbe: {
                exec: {
                    command: ["string"],
                },
                periodSeconds: 0,
                timeoutSeconds: 0,
            },
        },
        predictSchemata: {
            instanceSchemaUri: "string",
            parametersSchemaUri: "string",
            predictionSchemaUri: "string",
        },
    },
});
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type: google-native:aiplatform/v1:BatchPredictionJob
properties:
    dedicatedResources:
        machineSpec:
            acceleratorCount: 0
            acceleratorType: ACCELERATOR_TYPE_UNSPECIFIED
            machineType: string
            tpuTopology: string
        maxReplicaCount: 0
        startingReplicaCount: 0
    disableContainerLogging: false
    displayName: string
    encryptionSpec:
        kmsKeyName: string
    explanationSpec:
        metadata:
            featureAttributionsSchemaUri: string
            inputs:
                string: string
            latentSpaceSource: string
            outputs:
                string: string
        parameters:
            examples:
                exampleGcsSource:
                    dataFormat: DATA_FORMAT_UNSPECIFIED
                    gcsSource:
                        uris:
                            - string
                nearestNeighborSearchConfig: any
                neighborCount: 0
                presets:
                    modality: MODALITY_UNSPECIFIED
                    query: PRECISE
            integratedGradientsAttribution:
                blurBaselineConfig:
                    maxBlurSigma: 0
                smoothGradConfig:
                    featureNoiseSigma:
                        noiseSigma:
                            - name: string
                              sigma: 0
                    noiseSigma: 0
                    noisySampleCount: 0
                stepCount: 0
            outputIndices:
                - any
            sampledShapleyAttribution:
                pathCount: 0
            topK: 0
            xraiAttribution:
                blurBaselineConfig:
                    maxBlurSigma: 0
                smoothGradConfig:
                    featureNoiseSigma:
                        noiseSigma:
                            - name: string
                              sigma: 0
                    noiseSigma: 0
                    noisySampleCount: 0
                stepCount: 0
    generateExplanation: false
    inputConfig:
        bigquerySource:
            inputUri: string
        gcsSource:
            uris:
                - string
        instancesFormat: string
    instanceConfig:
        excludedFields:
            - string
        includedFields:
            - string
        instanceType: string
        keyField: string
    labels:
        string: string
    location: string
    manualBatchTuningParameters:
        batchSize: 0
    model: string
    modelParameters: any
    outputConfig:
        bigqueryDestination:
            outputUri: string
        gcsDestination:
            outputUriPrefix: string
        predictionsFormat: string
    project: string
    serviceAccount: string
    unmanagedContainerModel:
        artifactUri: string
        containerSpec:
            args:
                - string
            command:
                - string
            deploymentTimeout: string
            env:
                - name: string
                  value: string
            healthProbe:
                exec:
                    command:
                        - string
                periodSeconds: 0
                timeoutSeconds: 0
            healthRoute: string
            imageUri: string
            ports:
                - containerPort: 0
            predictRoute: string
            sharedMemorySizeMb: string
            startupProbe:
                exec:
                    command:
                        - string
                periodSeconds: 0
                timeoutSeconds: 0
        predictSchemata:
            instanceSchemaUri: string
            parametersSchemaUri: string
            predictionSchemaUri: string
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BatchPredictionJob Resource Properties

To learn more about resource properties and how to use them, see Inputs and Outputs in the Architecture and Concepts docs.

Inputs

In Python, inputs that are objects can be passed either as argument classes or as dictionary literals.

The BatchPredictionJob resource accepts the following input properties:

DisplayName This property is required. string
The user-defined name of this BatchPredictionJob.
InputConfig This property is required. Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1BatchPredictionJobInputConfig
Input configuration of the instances on which predictions are performed. The schema of any single instance may be specified via the Model's PredictSchemata's instance_schema_uri.
OutputConfig This property is required. Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1BatchPredictionJobOutputConfig
The Configuration specifying where output predictions should be written. The schema of any single prediction may be specified as a concatenation of Model's PredictSchemata's instance_schema_uri and prediction_schema_uri.
DedicatedResources Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1BatchDedicatedResources
The config of resources used by the Model during the batch prediction. If the Model supports DEDICATED_RESOURCES this config may be provided (and the job will use these resources), if the Model doesn't support AUTOMATIC_RESOURCES, this config must be provided.
DisableContainerLogging bool
For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send stderr and stdout streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to Cloud Logging pricing. User can disable container logging by setting this flag to true.
EncryptionSpec Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1EncryptionSpec
Customer-managed encryption key options for a BatchPredictionJob. If this is set, then all resources created by the BatchPredictionJob will be encrypted with the provided encryption key.
ExplanationSpec Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ExplanationSpec
Explanation configuration for this BatchPredictionJob. Can be specified only if generate_explanation is set to true. This value overrides the value of Model.explanation_spec. All fields of explanation_spec are optional in the request. If a field of the explanation_spec object is not populated, the corresponding field of the Model.explanation_spec object is inherited.
GenerateExplanation bool
Generate explanation with the batch prediction results. When set to true, the batch prediction output changes based on the predictions_format field of the BatchPredictionJob.output_config object: * bigquery: output includes a column named explanation. The value is a struct that conforms to the Explanation object. * jsonl: The JSON objects on each line include an additional entry keyed explanation. The value of the entry is a JSON object that conforms to the Explanation object. * csv: Generating explanations for CSV format is not supported. If this field is set to true, either the Model.explanation_spec or explanation_spec must be populated.
InstanceConfig Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1BatchPredictionJobInstanceConfig
Configuration for how to convert batch prediction input instances to the prediction instances that are sent to the Model.
Labels Dictionary<string, string>
The labels with user-defined metadata to organize BatchPredictionJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
Location Changes to this property will trigger replacement. string
ManualBatchTuningParameters Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ManualBatchTuningParameters
Immutable. Parameters configuring the batch behavior. Currently only applicable when dedicated_resources are used (in other cases Vertex AI does the tuning itself).
Model string
The name of the Model resource that produces the predictions via this job, must share the same ancestor Location. Starting this job has no impact on any existing deployments of the Model and their resources. Exactly one of model and unmanaged_container_model must be set. The model resource name may contain version id or version alias to specify the version. Example: projects/{project}/locations/{location}/models/{model}@2 or projects/{project}/locations/{location}/models/{model}@golden if no version is specified, the default version will be deployed. The model resource could also be a publisher model. Example: publishers/{publisher}/models/{model} or projects/{project}/locations/{location}/publishers/{publisher}/models/{model}
ModelParameters object
The parameters that govern the predictions. The schema of the parameters may be specified via the Model's PredictSchemata's parameters_schema_uri.
Project Changes to this property will trigger replacement. string
ServiceAccount string
The service account that the DeployedModel's container runs as. If not specified, a system generated one will be used, which has minimal permissions and the custom container, if used, may not have enough permission to access other Google Cloud resources. Users deploying the Model must have the iam.serviceAccounts.actAs permission on this service account.
UnmanagedContainerModel Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1UnmanagedContainerModel
Contains model information necessary to perform batch prediction without requiring uploading to model registry. Exactly one of model and unmanaged_container_model must be set.
DisplayName This property is required. string
The user-defined name of this BatchPredictionJob.
InputConfig This property is required. GoogleCloudAiplatformV1BatchPredictionJobInputConfigArgs
Input configuration of the instances on which predictions are performed. The schema of any single instance may be specified via the Model's PredictSchemata's instance_schema_uri.
OutputConfig This property is required. GoogleCloudAiplatformV1BatchPredictionJobOutputConfigArgs
The Configuration specifying where output predictions should be written. The schema of any single prediction may be specified as a concatenation of Model's PredictSchemata's instance_schema_uri and prediction_schema_uri.
DedicatedResources GoogleCloudAiplatformV1BatchDedicatedResourcesArgs
The config of resources used by the Model during the batch prediction. If the Model supports DEDICATED_RESOURCES this config may be provided (and the job will use these resources), if the Model doesn't support AUTOMATIC_RESOURCES, this config must be provided.
DisableContainerLogging bool
For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send stderr and stdout streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to Cloud Logging pricing. User can disable container logging by setting this flag to true.
EncryptionSpec GoogleCloudAiplatformV1EncryptionSpecArgs
Customer-managed encryption key options for a BatchPredictionJob. If this is set, then all resources created by the BatchPredictionJob will be encrypted with the provided encryption key.
ExplanationSpec GoogleCloudAiplatformV1ExplanationSpecArgs
Explanation configuration for this BatchPredictionJob. Can be specified only if generate_explanation is set to true. This value overrides the value of Model.explanation_spec. All fields of explanation_spec are optional in the request. If a field of the explanation_spec object is not populated, the corresponding field of the Model.explanation_spec object is inherited.
GenerateExplanation bool
Generate explanation with the batch prediction results. When set to true, the batch prediction output changes based on the predictions_format field of the BatchPredictionJob.output_config object: * bigquery: output includes a column named explanation. The value is a struct that conforms to the Explanation object. * jsonl: The JSON objects on each line include an additional entry keyed explanation. The value of the entry is a JSON object that conforms to the Explanation object. * csv: Generating explanations for CSV format is not supported. If this field is set to true, either the Model.explanation_spec or explanation_spec must be populated.
InstanceConfig GoogleCloudAiplatformV1BatchPredictionJobInstanceConfigArgs
Configuration for how to convert batch prediction input instances to the prediction instances that are sent to the Model.
Labels map[string]string
The labels with user-defined metadata to organize BatchPredictionJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
Location Changes to this property will trigger replacement. string
ManualBatchTuningParameters GoogleCloudAiplatformV1ManualBatchTuningParametersArgs
Immutable. Parameters configuring the batch behavior. Currently only applicable when dedicated_resources are used (in other cases Vertex AI does the tuning itself).
Model string
The name of the Model resource that produces the predictions via this job, must share the same ancestor Location. Starting this job has no impact on any existing deployments of the Model and their resources. Exactly one of model and unmanaged_container_model must be set. The model resource name may contain version id or version alias to specify the version. Example: projects/{project}/locations/{location}/models/{model}@2 or projects/{project}/locations/{location}/models/{model}@golden if no version is specified, the default version will be deployed. The model resource could also be a publisher model. Example: publishers/{publisher}/models/{model} or projects/{project}/locations/{location}/publishers/{publisher}/models/{model}
ModelParameters interface{}
The parameters that govern the predictions. The schema of the parameters may be specified via the Model's PredictSchemata's parameters_schema_uri.
Project Changes to this property will trigger replacement. string
ServiceAccount string
The service account that the DeployedModel's container runs as. If not specified, a system generated one will be used, which has minimal permissions and the custom container, if used, may not have enough permission to access other Google Cloud resources. Users deploying the Model must have the iam.serviceAccounts.actAs permission on this service account.
UnmanagedContainerModel GoogleCloudAiplatformV1UnmanagedContainerModelArgs
Contains model information necessary to perform batch prediction without requiring uploading to model registry. Exactly one of model and unmanaged_container_model must be set.
displayName This property is required. String
The user-defined name of this BatchPredictionJob.
inputConfig This property is required. GoogleCloudAiplatformV1BatchPredictionJobInputConfig
Input configuration of the instances on which predictions are performed. The schema of any single instance may be specified via the Model's PredictSchemata's instance_schema_uri.
outputConfig This property is required. GoogleCloudAiplatformV1BatchPredictionJobOutputConfig
The Configuration specifying where output predictions should be written. The schema of any single prediction may be specified as a concatenation of Model's PredictSchemata's instance_schema_uri and prediction_schema_uri.
dedicatedResources GoogleCloudAiplatformV1BatchDedicatedResources
The config of resources used by the Model during the batch prediction. If the Model supports DEDICATED_RESOURCES this config may be provided (and the job will use these resources), if the Model doesn't support AUTOMATIC_RESOURCES, this config must be provided.
disableContainerLogging Boolean
For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send stderr and stdout streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to Cloud Logging pricing. User can disable container logging by setting this flag to true.
encryptionSpec GoogleCloudAiplatformV1EncryptionSpec
Customer-managed encryption key options for a BatchPredictionJob. If this is set, then all resources created by the BatchPredictionJob will be encrypted with the provided encryption key.
explanationSpec GoogleCloudAiplatformV1ExplanationSpec
Explanation configuration for this BatchPredictionJob. Can be specified only if generate_explanation is set to true. This value overrides the value of Model.explanation_spec. All fields of explanation_spec are optional in the request. If a field of the explanation_spec object is not populated, the corresponding field of the Model.explanation_spec object is inherited.
generateExplanation Boolean
Generate explanation with the batch prediction results. When set to true, the batch prediction output changes based on the predictions_format field of the BatchPredictionJob.output_config object: * bigquery: output includes a column named explanation. The value is a struct that conforms to the Explanation object. * jsonl: The JSON objects on each line include an additional entry keyed explanation. The value of the entry is a JSON object that conforms to the Explanation object. * csv: Generating explanations for CSV format is not supported. If this field is set to true, either the Model.explanation_spec or explanation_spec must be populated.
instanceConfig GoogleCloudAiplatformV1BatchPredictionJobInstanceConfig
Configuration for how to convert batch prediction input instances to the prediction instances that are sent to the Model.
labels Map<String,String>
The labels with user-defined metadata to organize BatchPredictionJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
location Changes to this property will trigger replacement. String
manualBatchTuningParameters GoogleCloudAiplatformV1ManualBatchTuningParameters
Immutable. Parameters configuring the batch behavior. Currently only applicable when dedicated_resources are used (in other cases Vertex AI does the tuning itself).
model String
The name of the Model resource that produces the predictions via this job, must share the same ancestor Location. Starting this job has no impact on any existing deployments of the Model and their resources. Exactly one of model and unmanaged_container_model must be set. The model resource name may contain version id or version alias to specify the version. Example: projects/{project}/locations/{location}/models/{model}@2 or projects/{project}/locations/{location}/models/{model}@golden if no version is specified, the default version will be deployed. The model resource could also be a publisher model. Example: publishers/{publisher}/models/{model} or projects/{project}/locations/{location}/publishers/{publisher}/models/{model}
modelParameters Object
The parameters that govern the predictions. The schema of the parameters may be specified via the Model's PredictSchemata's parameters_schema_uri.
project Changes to this property will trigger replacement. String
serviceAccount String
The service account that the DeployedModel's container runs as. If not specified, a system generated one will be used, which has minimal permissions and the custom container, if used, may not have enough permission to access other Google Cloud resources. Users deploying the Model must have the iam.serviceAccounts.actAs permission on this service account.
unmanagedContainerModel GoogleCloudAiplatformV1UnmanagedContainerModel
Contains model information necessary to perform batch prediction without requiring uploading to model registry. Exactly one of model and unmanaged_container_model must be set.
displayName This property is required. string
The user-defined name of this BatchPredictionJob.
inputConfig This property is required. GoogleCloudAiplatformV1BatchPredictionJobInputConfig
Input configuration of the instances on which predictions are performed. The schema of any single instance may be specified via the Model's PredictSchemata's instance_schema_uri.
outputConfig This property is required. GoogleCloudAiplatformV1BatchPredictionJobOutputConfig
The Configuration specifying where output predictions should be written. The schema of any single prediction may be specified as a concatenation of Model's PredictSchemata's instance_schema_uri and prediction_schema_uri.
dedicatedResources GoogleCloudAiplatformV1BatchDedicatedResources
The config of resources used by the Model during the batch prediction. If the Model supports DEDICATED_RESOURCES this config may be provided (and the job will use these resources), if the Model doesn't support AUTOMATIC_RESOURCES, this config must be provided.
disableContainerLogging boolean
For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send stderr and stdout streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to Cloud Logging pricing. User can disable container logging by setting this flag to true.
encryptionSpec GoogleCloudAiplatformV1EncryptionSpec
Customer-managed encryption key options for a BatchPredictionJob. If this is set, then all resources created by the BatchPredictionJob will be encrypted with the provided encryption key.
explanationSpec GoogleCloudAiplatformV1ExplanationSpec
Explanation configuration for this BatchPredictionJob. Can be specified only if generate_explanation is set to true. This value overrides the value of Model.explanation_spec. All fields of explanation_spec are optional in the request. If a field of the explanation_spec object is not populated, the corresponding field of the Model.explanation_spec object is inherited.
generateExplanation boolean
Generate explanation with the batch prediction results. When set to true, the batch prediction output changes based on the predictions_format field of the BatchPredictionJob.output_config object: * bigquery: output includes a column named explanation. The value is a struct that conforms to the Explanation object. * jsonl: The JSON objects on each line include an additional entry keyed explanation. The value of the entry is a JSON object that conforms to the Explanation object. * csv: Generating explanations for CSV format is not supported. If this field is set to true, either the Model.explanation_spec or explanation_spec must be populated.
instanceConfig GoogleCloudAiplatformV1BatchPredictionJobInstanceConfig
Configuration for how to convert batch prediction input instances to the prediction instances that are sent to the Model.
labels {[key: string]: string}
The labels with user-defined metadata to organize BatchPredictionJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
location Changes to this property will trigger replacement. string
manualBatchTuningParameters GoogleCloudAiplatformV1ManualBatchTuningParameters
Immutable. Parameters configuring the batch behavior. Currently only applicable when dedicated_resources are used (in other cases Vertex AI does the tuning itself).
model string
The name of the Model resource that produces the predictions via this job, must share the same ancestor Location. Starting this job has no impact on any existing deployments of the Model and their resources. Exactly one of model and unmanaged_container_model must be set. The model resource name may contain version id or version alias to specify the version. Example: projects/{project}/locations/{location}/models/{model}@2 or projects/{project}/locations/{location}/models/{model}@golden if no version is specified, the default version will be deployed. The model resource could also be a publisher model. Example: publishers/{publisher}/models/{model} or projects/{project}/locations/{location}/publishers/{publisher}/models/{model}
modelParameters any
The parameters that govern the predictions. The schema of the parameters may be specified via the Model's PredictSchemata's parameters_schema_uri.
project Changes to this property will trigger replacement. string
serviceAccount string
The service account that the DeployedModel's container runs as. If not specified, a system generated one will be used, which has minimal permissions and the custom container, if used, may not have enough permission to access other Google Cloud resources. Users deploying the Model must have the iam.serviceAccounts.actAs permission on this service account.
unmanagedContainerModel GoogleCloudAiplatformV1UnmanagedContainerModel
Contains model information necessary to perform batch prediction without requiring uploading to model registry. Exactly one of model and unmanaged_container_model must be set.
display_name This property is required. str
The user-defined name of this BatchPredictionJob.
input_config This property is required. GoogleCloudAiplatformV1BatchPredictionJobInputConfigArgs
Input configuration of the instances on which predictions are performed. The schema of any single instance may be specified via the Model's PredictSchemata's instance_schema_uri.
output_config This property is required. GoogleCloudAiplatformV1BatchPredictionJobOutputConfigArgs
The Configuration specifying where output predictions should be written. The schema of any single prediction may be specified as a concatenation of Model's PredictSchemata's instance_schema_uri and prediction_schema_uri.
dedicated_resources GoogleCloudAiplatformV1BatchDedicatedResourcesArgs
The config of resources used by the Model during the batch prediction. If the Model supports DEDICATED_RESOURCES this config may be provided (and the job will use these resources), if the Model doesn't support AUTOMATIC_RESOURCES, this config must be provided.
disable_container_logging bool
For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send stderr and stdout streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to Cloud Logging pricing. User can disable container logging by setting this flag to true.
encryption_spec GoogleCloudAiplatformV1EncryptionSpecArgs
Customer-managed encryption key options for a BatchPredictionJob. If this is set, then all resources created by the BatchPredictionJob will be encrypted with the provided encryption key.
explanation_spec GoogleCloudAiplatformV1ExplanationSpecArgs
Explanation configuration for this BatchPredictionJob. Can be specified only if generate_explanation is set to true. This value overrides the value of Model.explanation_spec. All fields of explanation_spec are optional in the request. If a field of the explanation_spec object is not populated, the corresponding field of the Model.explanation_spec object is inherited.
generate_explanation bool
Generate explanation with the batch prediction results. When set to true, the batch prediction output changes based on the predictions_format field of the BatchPredictionJob.output_config object: * bigquery: output includes a column named explanation. The value is a struct that conforms to the Explanation object. * jsonl: The JSON objects on each line include an additional entry keyed explanation. The value of the entry is a JSON object that conforms to the Explanation object. * csv: Generating explanations for CSV format is not supported. If this field is set to true, either the Model.explanation_spec or explanation_spec must be populated.
instance_config GoogleCloudAiplatformV1BatchPredictionJobInstanceConfigArgs
Configuration for how to convert batch prediction input instances to the prediction instances that are sent to the Model.
labels Mapping[str, str]
The labels with user-defined metadata to organize BatchPredictionJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
location Changes to this property will trigger replacement. str
manual_batch_tuning_parameters GoogleCloudAiplatformV1ManualBatchTuningParametersArgs
Immutable. Parameters configuring the batch behavior. Currently only applicable when dedicated_resources are used (in other cases Vertex AI does the tuning itself).
model str
The name of the Model resource that produces the predictions via this job, must share the same ancestor Location. Starting this job has no impact on any existing deployments of the Model and their resources. Exactly one of model and unmanaged_container_model must be set. The model resource name may contain version id or version alias to specify the version. Example: projects/{project}/locations/{location}/models/{model}@2 or projects/{project}/locations/{location}/models/{model}@golden if no version is specified, the default version will be deployed. The model resource could also be a publisher model. Example: publishers/{publisher}/models/{model} or projects/{project}/locations/{location}/publishers/{publisher}/models/{model}
model_parameters Any
The parameters that govern the predictions. The schema of the parameters may be specified via the Model's PredictSchemata's parameters_schema_uri.
project Changes to this property will trigger replacement. str
service_account str
The service account that the DeployedModel's container runs as. If not specified, a system generated one will be used, which has minimal permissions and the custom container, if used, may not have enough permission to access other Google Cloud resources. Users deploying the Model must have the iam.serviceAccounts.actAs permission on this service account.
unmanaged_container_model GoogleCloudAiplatformV1UnmanagedContainerModelArgs
Contains model information necessary to perform batch prediction without requiring uploading to model registry. Exactly one of model and unmanaged_container_model must be set.
displayName This property is required. String
The user-defined name of this BatchPredictionJob.
inputConfig This property is required. Property Map
Input configuration of the instances on which predictions are performed. The schema of any single instance may be specified via the Model's PredictSchemata's instance_schema_uri.
outputConfig This property is required. Property Map
The Configuration specifying where output predictions should be written. The schema of any single prediction may be specified as a concatenation of Model's PredictSchemata's instance_schema_uri and prediction_schema_uri.
dedicatedResources Property Map
The config of resources used by the Model during the batch prediction. If the Model supports DEDICATED_RESOURCES this config may be provided (and the job will use these resources), if the Model doesn't support AUTOMATIC_RESOURCES, this config must be provided.
disableContainerLogging Boolean
For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send stderr and stdout streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to Cloud Logging pricing. User can disable container logging by setting this flag to true.
encryptionSpec Property Map
Customer-managed encryption key options for a BatchPredictionJob. If this is set, then all resources created by the BatchPredictionJob will be encrypted with the provided encryption key.
explanationSpec Property Map
Explanation configuration for this BatchPredictionJob. Can be specified only if generate_explanation is set to true. This value overrides the value of Model.explanation_spec. All fields of explanation_spec are optional in the request. If a field of the explanation_spec object is not populated, the corresponding field of the Model.explanation_spec object is inherited.
generateExplanation Boolean
Generate explanation with the batch prediction results. When set to true, the batch prediction output changes based on the predictions_format field of the BatchPredictionJob.output_config object: * bigquery: output includes a column named explanation. The value is a struct that conforms to the Explanation object. * jsonl: The JSON objects on each line include an additional entry keyed explanation. The value of the entry is a JSON object that conforms to the Explanation object. * csv: Generating explanations for CSV format is not supported. If this field is set to true, either the Model.explanation_spec or explanation_spec must be populated.
instanceConfig Property Map
Configuration for how to convert batch prediction input instances to the prediction instances that are sent to the Model.
labels Map<String>
The labels with user-defined metadata to organize BatchPredictionJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
location Changes to this property will trigger replacement. String
manualBatchTuningParameters Property Map
Immutable. Parameters configuring the batch behavior. Currently only applicable when dedicated_resources are used (in other cases Vertex AI does the tuning itself).
model String
The name of the Model resource that produces the predictions via this job, must share the same ancestor Location. Starting this job has no impact on any existing deployments of the Model and their resources. Exactly one of model and unmanaged_container_model must be set. The model resource name may contain version id or version alias to specify the version. Example: projects/{project}/locations/{location}/models/{model}@2 or projects/{project}/locations/{location}/models/{model}@golden if no version is specified, the default version will be deployed. The model resource could also be a publisher model. Example: publishers/{publisher}/models/{model} or projects/{project}/locations/{location}/publishers/{publisher}/models/{model}
modelParameters Any
The parameters that govern the predictions. The schema of the parameters may be specified via the Model's PredictSchemata's parameters_schema_uri.
project Changes to this property will trigger replacement. String
serviceAccount String
The service account that the DeployedModel's container runs as. If not specified, a system generated one will be used, which has minimal permissions and the custom container, if used, may not have enough permission to access other Google Cloud resources. Users deploying the Model must have the iam.serviceAccounts.actAs permission on this service account.
unmanagedContainerModel Property Map
Contains model information necessary to perform batch prediction without requiring uploading to model registry. Exactly one of model and unmanaged_container_model must be set.

Outputs

All input properties are implicitly available as output properties. Additionally, the BatchPredictionJob resource produces the following output properties:

CompletionStats Pulumi.GoogleNative.Aiplatform.V1.Outputs.GoogleCloudAiplatformV1CompletionStatsResponse
Statistics on completed and failed prediction instances.
CreateTime string
Time when the BatchPredictionJob was created.
EndTime string
Time when the BatchPredictionJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.
Error Pulumi.GoogleNative.Aiplatform.V1.Outputs.GoogleRpcStatusResponse
Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
Id string
The provider-assigned unique ID for this managed resource.
ModelVersionId string
The version ID of the Model that produces the predictions via this job.
Name string
Resource name of the BatchPredictionJob.
OutputInfo Pulumi.GoogleNative.Aiplatform.V1.Outputs.GoogleCloudAiplatformV1BatchPredictionJobOutputInfoResponse
Information further describing the output of this job.
PartialFailures List<Pulumi.GoogleNative.Aiplatform.V1.Outputs.GoogleRpcStatusResponse>
Partial failures encountered. For example, single files that can't be read. This field never exceeds 20 entries. Status details fields contain standard Google Cloud error details.
ResourcesConsumed Pulumi.GoogleNative.Aiplatform.V1.Outputs.GoogleCloudAiplatformV1ResourcesConsumedResponse
Information about resources that had been consumed by this job. Provided in real time at best effort basis, as well as a final value once the job completes. Note: This field currently may be not populated for batch predictions that use AutoML Models.
StartTime string
Time when the BatchPredictionJob for the first time entered the JOB_STATE_RUNNING state.
State string
The detailed state of the job.
UpdateTime string
Time when the BatchPredictionJob was most recently updated.
CompletionStats GoogleCloudAiplatformV1CompletionStatsResponse
Statistics on completed and failed prediction instances.
CreateTime string
Time when the BatchPredictionJob was created.
EndTime string
Time when the BatchPredictionJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.
Error GoogleRpcStatusResponse
Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
Id string
The provider-assigned unique ID for this managed resource.
ModelVersionId string
The version ID of the Model that produces the predictions via this job.
Name string
Resource name of the BatchPredictionJob.
OutputInfo GoogleCloudAiplatformV1BatchPredictionJobOutputInfoResponse
Information further describing the output of this job.
PartialFailures []GoogleRpcStatusResponse
Partial failures encountered. For example, single files that can't be read. This field never exceeds 20 entries. Status details fields contain standard Google Cloud error details.
ResourcesConsumed GoogleCloudAiplatformV1ResourcesConsumedResponse
Information about resources that had been consumed by this job. Provided in real time at best effort basis, as well as a final value once the job completes. Note: This field currently may be not populated for batch predictions that use AutoML Models.
StartTime string
Time when the BatchPredictionJob for the first time entered the JOB_STATE_RUNNING state.
State string
The detailed state of the job.
UpdateTime string
Time when the BatchPredictionJob was most recently updated.
completionStats GoogleCloudAiplatformV1CompletionStatsResponse
Statistics on completed and failed prediction instances.
createTime String
Time when the BatchPredictionJob was created.
endTime String
Time when the BatchPredictionJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.
error GoogleRpcStatusResponse
Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
id String
The provider-assigned unique ID for this managed resource.
modelVersionId String
The version ID of the Model that produces the predictions via this job.
name String
Resource name of the BatchPredictionJob.
outputInfo GoogleCloudAiplatformV1BatchPredictionJobOutputInfoResponse
Information further describing the output of this job.
partialFailures List<GoogleRpcStatusResponse>
Partial failures encountered. For example, single files that can't be read. This field never exceeds 20 entries. Status details fields contain standard Google Cloud error details.
resourcesConsumed GoogleCloudAiplatformV1ResourcesConsumedResponse
Information about resources that had been consumed by this job. Provided in real time at best effort basis, as well as a final value once the job completes. Note: This field currently may be not populated for batch predictions that use AutoML Models.
startTime String
Time when the BatchPredictionJob for the first time entered the JOB_STATE_RUNNING state.
state String
The detailed state of the job.
updateTime String
Time when the BatchPredictionJob was most recently updated.
completionStats GoogleCloudAiplatformV1CompletionStatsResponse
Statistics on completed and failed prediction instances.
createTime string
Time when the BatchPredictionJob was created.
endTime string
Time when the BatchPredictionJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.
error GoogleRpcStatusResponse
Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
id string
The provider-assigned unique ID for this managed resource.
modelVersionId string
The version ID of the Model that produces the predictions via this job.
name string
Resource name of the BatchPredictionJob.
outputInfo GoogleCloudAiplatformV1BatchPredictionJobOutputInfoResponse
Information further describing the output of this job.
partialFailures GoogleRpcStatusResponse[]
Partial failures encountered. For example, single files that can't be read. This field never exceeds 20 entries. Status details fields contain standard Google Cloud error details.
resourcesConsumed GoogleCloudAiplatformV1ResourcesConsumedResponse
Information about resources that had been consumed by this job. Provided in real time at best effort basis, as well as a final value once the job completes. Note: This field currently may be not populated for batch predictions that use AutoML Models.
startTime string
Time when the BatchPredictionJob for the first time entered the JOB_STATE_RUNNING state.
state string
The detailed state of the job.
updateTime string
Time when the BatchPredictionJob was most recently updated.
completion_stats GoogleCloudAiplatformV1CompletionStatsResponse
Statistics on completed and failed prediction instances.
create_time str
Time when the BatchPredictionJob was created.
end_time str
Time when the BatchPredictionJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.
error GoogleRpcStatusResponse
Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
id str
The provider-assigned unique ID for this managed resource.
model_version_id str
The version ID of the Model that produces the predictions via this job.
name str
Resource name of the BatchPredictionJob.
output_info GoogleCloudAiplatformV1BatchPredictionJobOutputInfoResponse
Information further describing the output of this job.
partial_failures Sequence[GoogleRpcStatusResponse]
Partial failures encountered. For example, single files that can't be read. This field never exceeds 20 entries. Status details fields contain standard Google Cloud error details.
resources_consumed GoogleCloudAiplatformV1ResourcesConsumedResponse
Information about resources that had been consumed by this job. Provided in real time at best effort basis, as well as a final value once the job completes. Note: This field currently may be not populated for batch predictions that use AutoML Models.
start_time str
Time when the BatchPredictionJob for the first time entered the JOB_STATE_RUNNING state.
state str
The detailed state of the job.
update_time str
Time when the BatchPredictionJob was most recently updated.
completionStats Property Map
Statistics on completed and failed prediction instances.
createTime String
Time when the BatchPredictionJob was created.
endTime String
Time when the BatchPredictionJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.
error Property Map
Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
id String
The provider-assigned unique ID for this managed resource.
modelVersionId String
The version ID of the Model that produces the predictions via this job.
name String
Resource name of the BatchPredictionJob.
outputInfo Property Map
Information further describing the output of this job.
partialFailures List<Property Map>
Partial failures encountered. For example, single files that can't be read. This field never exceeds 20 entries. Status details fields contain standard Google Cloud error details.
resourcesConsumed Property Map
Information about resources that had been consumed by this job. Provided in real time at best effort basis, as well as a final value once the job completes. Note: This field currently may be not populated for batch predictions that use AutoML Models.
startTime String
Time when the BatchPredictionJob for the first time entered the JOB_STATE_RUNNING state.
state String
The detailed state of the job.
updateTime String
Time when the BatchPredictionJob was most recently updated.

Supporting Types

GoogleCloudAiplatformV1BatchDedicatedResources
, GoogleCloudAiplatformV1BatchDedicatedResourcesArgs

MachineSpec This property is required. Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1MachineSpec
Immutable. The specification of a single machine.
MaxReplicaCount int
Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
StartingReplicaCount int
Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count
MachineSpec This property is required. GoogleCloudAiplatformV1MachineSpec
Immutable. The specification of a single machine.
MaxReplicaCount int
Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
StartingReplicaCount int
Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count
machineSpec This property is required. GoogleCloudAiplatformV1MachineSpec
Immutable. The specification of a single machine.
maxReplicaCount Integer
Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
startingReplicaCount Integer
Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count
machineSpec This property is required. GoogleCloudAiplatformV1MachineSpec
Immutable. The specification of a single machine.
maxReplicaCount number
Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
startingReplicaCount number
Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count
machine_spec This property is required. GoogleCloudAiplatformV1MachineSpec
Immutable. The specification of a single machine.
max_replica_count int
Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
starting_replica_count int
Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count
machineSpec This property is required. Property Map
Immutable. The specification of a single machine.
maxReplicaCount Number
Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
startingReplicaCount Number
Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count

GoogleCloudAiplatformV1BatchDedicatedResourcesResponse
, GoogleCloudAiplatformV1BatchDedicatedResourcesResponseArgs

MachineSpec This property is required. Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1MachineSpecResponse
Immutable. The specification of a single machine.
MaxReplicaCount This property is required. int
Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
StartingReplicaCount This property is required. int
Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count
MachineSpec This property is required. GoogleCloudAiplatformV1MachineSpecResponse
Immutable. The specification of a single machine.
MaxReplicaCount This property is required. int
Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
StartingReplicaCount This property is required. int
Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count
machineSpec This property is required. GoogleCloudAiplatformV1MachineSpecResponse
Immutable. The specification of a single machine.
maxReplicaCount This property is required. Integer
Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
startingReplicaCount This property is required. Integer
Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count
machineSpec This property is required. GoogleCloudAiplatformV1MachineSpecResponse
Immutable. The specification of a single machine.
maxReplicaCount This property is required. number
Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
startingReplicaCount This property is required. number
Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count
machine_spec This property is required. GoogleCloudAiplatformV1MachineSpecResponse
Immutable. The specification of a single machine.
max_replica_count This property is required. int
Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
starting_replica_count This property is required. int
Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count
machineSpec This property is required. Property Map
Immutable. The specification of a single machine.
maxReplicaCount This property is required. Number
Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
startingReplicaCount This property is required. Number
Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count

GoogleCloudAiplatformV1BatchPredictionJobInputConfig
, GoogleCloudAiplatformV1BatchPredictionJobInputConfigArgs

InstancesFormat This property is required. string
The format in which instances are given, must be one of the Model's supported_input_storage_formats.
BigquerySource Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1BigQuerySource
The BigQuery location of the input table. The schema of the table should be in the format described by the given context OpenAPI Schema, if one is provided. The table may contain additional columns that are not described by the schema, and they will be ignored.
GcsSource Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1GcsSource
The Cloud Storage location for the input instances.
InstancesFormat This property is required. string
The format in which instances are given, must be one of the Model's supported_input_storage_formats.
BigquerySource GoogleCloudAiplatformV1BigQuerySource
The BigQuery location of the input table. The schema of the table should be in the format described by the given context OpenAPI Schema, if one is provided. The table may contain additional columns that are not described by the schema, and they will be ignored.
GcsSource GoogleCloudAiplatformV1GcsSource
The Cloud Storage location for the input instances.
instancesFormat This property is required. String
The format in which instances are given, must be one of the Model's supported_input_storage_formats.
bigquerySource GoogleCloudAiplatformV1BigQuerySource
The BigQuery location of the input table. The schema of the table should be in the format described by the given context OpenAPI Schema, if one is provided. The table may contain additional columns that are not described by the schema, and they will be ignored.
gcsSource GoogleCloudAiplatformV1GcsSource
The Cloud Storage location for the input instances.
instancesFormat This property is required. string
The format in which instances are given, must be one of the Model's supported_input_storage_formats.
bigquerySource GoogleCloudAiplatformV1BigQuerySource
The BigQuery location of the input table. The schema of the table should be in the format described by the given context OpenAPI Schema, if one is provided. The table may contain additional columns that are not described by the schema, and they will be ignored.
gcsSource GoogleCloudAiplatformV1GcsSource
The Cloud Storage location for the input instances.
instances_format This property is required. str
The format in which instances are given, must be one of the Model's supported_input_storage_formats.
bigquery_source GoogleCloudAiplatformV1BigQuerySource
The BigQuery location of the input table. The schema of the table should be in the format described by the given context OpenAPI Schema, if one is provided. The table may contain additional columns that are not described by the schema, and they will be ignored.
gcs_source GoogleCloudAiplatformV1GcsSource
The Cloud Storage location for the input instances.
instancesFormat This property is required. String
The format in which instances are given, must be one of the Model's supported_input_storage_formats.
bigquerySource Property Map
The BigQuery location of the input table. The schema of the table should be in the format described by the given context OpenAPI Schema, if one is provided. The table may contain additional columns that are not described by the schema, and they will be ignored.
gcsSource Property Map
The Cloud Storage location for the input instances.

GoogleCloudAiplatformV1BatchPredictionJobInputConfigResponse
, GoogleCloudAiplatformV1BatchPredictionJobInputConfigResponseArgs

BigquerySource This property is required. Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1BigQuerySourceResponse
The BigQuery location of the input table. The schema of the table should be in the format described by the given context OpenAPI Schema, if one is provided. The table may contain additional columns that are not described by the schema, and they will be ignored.
GcsSource This property is required. Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1GcsSourceResponse
The Cloud Storage location for the input instances.
InstancesFormat This property is required. string
The format in which instances are given, must be one of the Model's supported_input_storage_formats.
BigquerySource This property is required. GoogleCloudAiplatformV1BigQuerySourceResponse
The BigQuery location of the input table. The schema of the table should be in the format described by the given context OpenAPI Schema, if one is provided. The table may contain additional columns that are not described by the schema, and they will be ignored.
GcsSource This property is required. GoogleCloudAiplatformV1GcsSourceResponse
The Cloud Storage location for the input instances.
InstancesFormat This property is required. string
The format in which instances are given, must be one of the Model's supported_input_storage_formats.
bigquerySource This property is required. GoogleCloudAiplatformV1BigQuerySourceResponse
The BigQuery location of the input table. The schema of the table should be in the format described by the given context OpenAPI Schema, if one is provided. The table may contain additional columns that are not described by the schema, and they will be ignored.
gcsSource This property is required. GoogleCloudAiplatformV1GcsSourceResponse
The Cloud Storage location for the input instances.
instancesFormat This property is required. String
The format in which instances are given, must be one of the Model's supported_input_storage_formats.
bigquerySource This property is required. GoogleCloudAiplatformV1BigQuerySourceResponse
The BigQuery location of the input table. The schema of the table should be in the format described by the given context OpenAPI Schema, if one is provided. The table may contain additional columns that are not described by the schema, and they will be ignored.
gcsSource This property is required. GoogleCloudAiplatformV1GcsSourceResponse
The Cloud Storage location for the input instances.
instancesFormat This property is required. string
The format in which instances are given, must be one of the Model's supported_input_storage_formats.
bigquery_source This property is required. GoogleCloudAiplatformV1BigQuerySourceResponse
The BigQuery location of the input table. The schema of the table should be in the format described by the given context OpenAPI Schema, if one is provided. The table may contain additional columns that are not described by the schema, and they will be ignored.
gcs_source This property is required. GoogleCloudAiplatformV1GcsSourceResponse
The Cloud Storage location for the input instances.
instances_format This property is required. str
The format in which instances are given, must be one of the Model's supported_input_storage_formats.
bigquerySource This property is required. Property Map
The BigQuery location of the input table. The schema of the table should be in the format described by the given context OpenAPI Schema, if one is provided. The table may contain additional columns that are not described by the schema, and they will be ignored.
gcsSource This property is required. Property Map
The Cloud Storage location for the input instances.
instancesFormat This property is required. String
The format in which instances are given, must be one of the Model's supported_input_storage_formats.

GoogleCloudAiplatformV1BatchPredictionJobInstanceConfig
, GoogleCloudAiplatformV1BatchPredictionJobInstanceConfigArgs

ExcludedFields List<string>
Fields that will be excluded in the prediction instance that is sent to the Model. Excluded will be attached to the batch prediction output if key_field is not specified. When excluded_fields is populated, included_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
IncludedFields List<string>
Fields that will be included in the prediction instance that is sent to the Model. If instance_type is array, the order of field names in included_fields also determines the order of the values in the array. When included_fields is populated, excluded_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
InstanceType string
The format of the instance that the Model accepts. Vertex AI will convert compatible batch prediction input instance formats to the specified format. Supported values are: * object: Each input is converted to JSON object format. * For bigquery, each row is converted to an object. * For jsonl, each line of the JSONL input must be an object. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. * array: Each input is converted to JSON array format. * For bigquery, each row is converted to an array. The order of columns is determined by the BigQuery column order, unless included_fields is populated. included_fields must be populated for specifying field orders. * For jsonl, if each line of the JSONL input is an object, included_fields must be populated for specifying field orders. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. If not specified, Vertex AI converts the batch prediction input as follows: * For bigquery and csv, the behavior is the same as array. The order of columns is the same as defined in the file or table, unless included_fields is populated. * For jsonl, the prediction instance format is determined by each line of the input. * For tf-record/tf-record-gzip, each record will be converted to an object in the format of {"b64": }, where is the Base64-encoded string of the content of the record. * For `file-list`, each file in the list will be converted to an object in the format of `{"b64": }`, where is the Base64-encoded string of the content of the file.
KeyField string
The name of the field that is considered as a key. The values identified by the key field is not included in the transformed instances that is sent to the Model. This is similar to specifying this name of the field in excluded_fields. In addition, the batch prediction output will not include the instances. Instead the output will only include the value of the key field, in a field named key in the output: * For jsonl output format, the output will have a key field instead of the instance field. * For csv/bigquery output format, the output will have have a key column instead of the instance feature columns. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
ExcludedFields []string
Fields that will be excluded in the prediction instance that is sent to the Model. Excluded will be attached to the batch prediction output if key_field is not specified. When excluded_fields is populated, included_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
IncludedFields []string
Fields that will be included in the prediction instance that is sent to the Model. If instance_type is array, the order of field names in included_fields also determines the order of the values in the array. When included_fields is populated, excluded_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
InstanceType string
The format of the instance that the Model accepts. Vertex AI will convert compatible batch prediction input instance formats to the specified format. Supported values are: * object: Each input is converted to JSON object format. * For bigquery, each row is converted to an object. * For jsonl, each line of the JSONL input must be an object. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. * array: Each input is converted to JSON array format. * For bigquery, each row is converted to an array. The order of columns is determined by the BigQuery column order, unless included_fields is populated. included_fields must be populated for specifying field orders. * For jsonl, if each line of the JSONL input is an object, included_fields must be populated for specifying field orders. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. If not specified, Vertex AI converts the batch prediction input as follows: * For bigquery and csv, the behavior is the same as array. The order of columns is the same as defined in the file or table, unless included_fields is populated. * For jsonl, the prediction instance format is determined by each line of the input. * For tf-record/tf-record-gzip, each record will be converted to an object in the format of {"b64": }, where is the Base64-encoded string of the content of the record. * For `file-list`, each file in the list will be converted to an object in the format of `{"b64": }`, where is the Base64-encoded string of the content of the file.
KeyField string
The name of the field that is considered as a key. The values identified by the key field is not included in the transformed instances that is sent to the Model. This is similar to specifying this name of the field in excluded_fields. In addition, the batch prediction output will not include the instances. Instead the output will only include the value of the key field, in a field named key in the output: * For jsonl output format, the output will have a key field instead of the instance field. * For csv/bigquery output format, the output will have have a key column instead of the instance feature columns. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
excludedFields List<String>
Fields that will be excluded in the prediction instance that is sent to the Model. Excluded will be attached to the batch prediction output if key_field is not specified. When excluded_fields is populated, included_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
includedFields List<String>
Fields that will be included in the prediction instance that is sent to the Model. If instance_type is array, the order of field names in included_fields also determines the order of the values in the array. When included_fields is populated, excluded_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
instanceType String
The format of the instance that the Model accepts. Vertex AI will convert compatible batch prediction input instance formats to the specified format. Supported values are: * object: Each input is converted to JSON object format. * For bigquery, each row is converted to an object. * For jsonl, each line of the JSONL input must be an object. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. * array: Each input is converted to JSON array format. * For bigquery, each row is converted to an array. The order of columns is determined by the BigQuery column order, unless included_fields is populated. included_fields must be populated for specifying field orders. * For jsonl, if each line of the JSONL input is an object, included_fields must be populated for specifying field orders. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. If not specified, Vertex AI converts the batch prediction input as follows: * For bigquery and csv, the behavior is the same as array. The order of columns is the same as defined in the file or table, unless included_fields is populated. * For jsonl, the prediction instance format is determined by each line of the input. * For tf-record/tf-record-gzip, each record will be converted to an object in the format of {"b64": }, where is the Base64-encoded string of the content of the record. * For `file-list`, each file in the list will be converted to an object in the format of `{"b64": }`, where is the Base64-encoded string of the content of the file.
keyField String
The name of the field that is considered as a key. The values identified by the key field is not included in the transformed instances that is sent to the Model. This is similar to specifying this name of the field in excluded_fields. In addition, the batch prediction output will not include the instances. Instead the output will only include the value of the key field, in a field named key in the output: * For jsonl output format, the output will have a key field instead of the instance field. * For csv/bigquery output format, the output will have have a key column instead of the instance feature columns. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
excludedFields string[]
Fields that will be excluded in the prediction instance that is sent to the Model. Excluded will be attached to the batch prediction output if key_field is not specified. When excluded_fields is populated, included_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
includedFields string[]
Fields that will be included in the prediction instance that is sent to the Model. If instance_type is array, the order of field names in included_fields also determines the order of the values in the array. When included_fields is populated, excluded_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
instanceType string
The format of the instance that the Model accepts. Vertex AI will convert compatible batch prediction input instance formats to the specified format. Supported values are: * object: Each input is converted to JSON object format. * For bigquery, each row is converted to an object. * For jsonl, each line of the JSONL input must be an object. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. * array: Each input is converted to JSON array format. * For bigquery, each row is converted to an array. The order of columns is determined by the BigQuery column order, unless included_fields is populated. included_fields must be populated for specifying field orders. * For jsonl, if each line of the JSONL input is an object, included_fields must be populated for specifying field orders. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. If not specified, Vertex AI converts the batch prediction input as follows: * For bigquery and csv, the behavior is the same as array. The order of columns is the same as defined in the file or table, unless included_fields is populated. * For jsonl, the prediction instance format is determined by each line of the input. * For tf-record/tf-record-gzip, each record will be converted to an object in the format of {"b64": }, where is the Base64-encoded string of the content of the record. * For `file-list`, each file in the list will be converted to an object in the format of `{"b64": }`, where is the Base64-encoded string of the content of the file.
keyField string
The name of the field that is considered as a key. The values identified by the key field is not included in the transformed instances that is sent to the Model. This is similar to specifying this name of the field in excluded_fields. In addition, the batch prediction output will not include the instances. Instead the output will only include the value of the key field, in a field named key in the output: * For jsonl output format, the output will have a key field instead of the instance field. * For csv/bigquery output format, the output will have have a key column instead of the instance feature columns. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
excluded_fields Sequence[str]
Fields that will be excluded in the prediction instance that is sent to the Model. Excluded will be attached to the batch prediction output if key_field is not specified. When excluded_fields is populated, included_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
included_fields Sequence[str]
Fields that will be included in the prediction instance that is sent to the Model. If instance_type is array, the order of field names in included_fields also determines the order of the values in the array. When included_fields is populated, excluded_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
instance_type str
The format of the instance that the Model accepts. Vertex AI will convert compatible batch prediction input instance formats to the specified format. Supported values are: * object: Each input is converted to JSON object format. * For bigquery, each row is converted to an object. * For jsonl, each line of the JSONL input must be an object. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. * array: Each input is converted to JSON array format. * For bigquery, each row is converted to an array. The order of columns is determined by the BigQuery column order, unless included_fields is populated. included_fields must be populated for specifying field orders. * For jsonl, if each line of the JSONL input is an object, included_fields must be populated for specifying field orders. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. If not specified, Vertex AI converts the batch prediction input as follows: * For bigquery and csv, the behavior is the same as array. The order of columns is the same as defined in the file or table, unless included_fields is populated. * For jsonl, the prediction instance format is determined by each line of the input. * For tf-record/tf-record-gzip, each record will be converted to an object in the format of {"b64": }, where is the Base64-encoded string of the content of the record. * For `file-list`, each file in the list will be converted to an object in the format of `{"b64": }`, where is the Base64-encoded string of the content of the file.
key_field str
The name of the field that is considered as a key. The values identified by the key field is not included in the transformed instances that is sent to the Model. This is similar to specifying this name of the field in excluded_fields. In addition, the batch prediction output will not include the instances. Instead the output will only include the value of the key field, in a field named key in the output: * For jsonl output format, the output will have a key field instead of the instance field. * For csv/bigquery output format, the output will have have a key column instead of the instance feature columns. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
excludedFields List<String>
Fields that will be excluded in the prediction instance that is sent to the Model. Excluded will be attached to the batch prediction output if key_field is not specified. When excluded_fields is populated, included_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
includedFields List<String>
Fields that will be included in the prediction instance that is sent to the Model. If instance_type is array, the order of field names in included_fields also determines the order of the values in the array. When included_fields is populated, excluded_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
instanceType String
The format of the instance that the Model accepts. Vertex AI will convert compatible batch prediction input instance formats to the specified format. Supported values are: * object: Each input is converted to JSON object format. * For bigquery, each row is converted to an object. * For jsonl, each line of the JSONL input must be an object. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. * array: Each input is converted to JSON array format. * For bigquery, each row is converted to an array. The order of columns is determined by the BigQuery column order, unless included_fields is populated. included_fields must be populated for specifying field orders. * For jsonl, if each line of the JSONL input is an object, included_fields must be populated for specifying field orders. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. If not specified, Vertex AI converts the batch prediction input as follows: * For bigquery and csv, the behavior is the same as array. The order of columns is the same as defined in the file or table, unless included_fields is populated. * For jsonl, the prediction instance format is determined by each line of the input. * For tf-record/tf-record-gzip, each record will be converted to an object in the format of {"b64": }, where is the Base64-encoded string of the content of the record. * For `file-list`, each file in the list will be converted to an object in the format of `{"b64": }`, where is the Base64-encoded string of the content of the file.
keyField String
The name of the field that is considered as a key. The values identified by the key field is not included in the transformed instances that is sent to the Model. This is similar to specifying this name of the field in excluded_fields. In addition, the batch prediction output will not include the instances. Instead the output will only include the value of the key field, in a field named key in the output: * For jsonl output format, the output will have a key field instead of the instance field. * For csv/bigquery output format, the output will have have a key column instead of the instance feature columns. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.

GoogleCloudAiplatformV1BatchPredictionJobInstanceConfigResponse
, GoogleCloudAiplatformV1BatchPredictionJobInstanceConfigResponseArgs

ExcludedFields This property is required. List<string>
Fields that will be excluded in the prediction instance that is sent to the Model. Excluded will be attached to the batch prediction output if key_field is not specified. When excluded_fields is populated, included_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
IncludedFields This property is required. List<string>
Fields that will be included in the prediction instance that is sent to the Model. If instance_type is array, the order of field names in included_fields also determines the order of the values in the array. When included_fields is populated, excluded_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
InstanceType This property is required. string
The format of the instance that the Model accepts. Vertex AI will convert compatible batch prediction input instance formats to the specified format. Supported values are: * object: Each input is converted to JSON object format. * For bigquery, each row is converted to an object. * For jsonl, each line of the JSONL input must be an object. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. * array: Each input is converted to JSON array format. * For bigquery, each row is converted to an array. The order of columns is determined by the BigQuery column order, unless included_fields is populated. included_fields must be populated for specifying field orders. * For jsonl, if each line of the JSONL input is an object, included_fields must be populated for specifying field orders. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. If not specified, Vertex AI converts the batch prediction input as follows: * For bigquery and csv, the behavior is the same as array. The order of columns is the same as defined in the file or table, unless included_fields is populated. * For jsonl, the prediction instance format is determined by each line of the input. * For tf-record/tf-record-gzip, each record will be converted to an object in the format of {"b64": }, where is the Base64-encoded string of the content of the record. * For `file-list`, each file in the list will be converted to an object in the format of `{"b64": }`, where is the Base64-encoded string of the content of the file.
KeyField This property is required. string
The name of the field that is considered as a key. The values identified by the key field is not included in the transformed instances that is sent to the Model. This is similar to specifying this name of the field in excluded_fields. In addition, the batch prediction output will not include the instances. Instead the output will only include the value of the key field, in a field named key in the output: * For jsonl output format, the output will have a key field instead of the instance field. * For csv/bigquery output format, the output will have have a key column instead of the instance feature columns. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
ExcludedFields This property is required. []string
Fields that will be excluded in the prediction instance that is sent to the Model. Excluded will be attached to the batch prediction output if key_field is not specified. When excluded_fields is populated, included_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
IncludedFields This property is required. []string
Fields that will be included in the prediction instance that is sent to the Model. If instance_type is array, the order of field names in included_fields also determines the order of the values in the array. When included_fields is populated, excluded_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
InstanceType This property is required. string
The format of the instance that the Model accepts. Vertex AI will convert compatible batch prediction input instance formats to the specified format. Supported values are: * object: Each input is converted to JSON object format. * For bigquery, each row is converted to an object. * For jsonl, each line of the JSONL input must be an object. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. * array: Each input is converted to JSON array format. * For bigquery, each row is converted to an array. The order of columns is determined by the BigQuery column order, unless included_fields is populated. included_fields must be populated for specifying field orders. * For jsonl, if each line of the JSONL input is an object, included_fields must be populated for specifying field orders. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. If not specified, Vertex AI converts the batch prediction input as follows: * For bigquery and csv, the behavior is the same as array. The order of columns is the same as defined in the file or table, unless included_fields is populated. * For jsonl, the prediction instance format is determined by each line of the input. * For tf-record/tf-record-gzip, each record will be converted to an object in the format of {"b64": }, where is the Base64-encoded string of the content of the record. * For `file-list`, each file in the list will be converted to an object in the format of `{"b64": }`, where is the Base64-encoded string of the content of the file.
KeyField This property is required. string
The name of the field that is considered as a key. The values identified by the key field is not included in the transformed instances that is sent to the Model. This is similar to specifying this name of the field in excluded_fields. In addition, the batch prediction output will not include the instances. Instead the output will only include the value of the key field, in a field named key in the output: * For jsonl output format, the output will have a key field instead of the instance field. * For csv/bigquery output format, the output will have have a key column instead of the instance feature columns. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
excludedFields This property is required. List<String>
Fields that will be excluded in the prediction instance that is sent to the Model. Excluded will be attached to the batch prediction output if key_field is not specified. When excluded_fields is populated, included_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
includedFields This property is required. List<String>
Fields that will be included in the prediction instance that is sent to the Model. If instance_type is array, the order of field names in included_fields also determines the order of the values in the array. When included_fields is populated, excluded_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
instanceType This property is required. String
The format of the instance that the Model accepts. Vertex AI will convert compatible batch prediction input instance formats to the specified format. Supported values are: * object: Each input is converted to JSON object format. * For bigquery, each row is converted to an object. * For jsonl, each line of the JSONL input must be an object. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. * array: Each input is converted to JSON array format. * For bigquery, each row is converted to an array. The order of columns is determined by the BigQuery column order, unless included_fields is populated. included_fields must be populated for specifying field orders. * For jsonl, if each line of the JSONL input is an object, included_fields must be populated for specifying field orders. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. If not specified, Vertex AI converts the batch prediction input as follows: * For bigquery and csv, the behavior is the same as array. The order of columns is the same as defined in the file or table, unless included_fields is populated. * For jsonl, the prediction instance format is determined by each line of the input. * For tf-record/tf-record-gzip, each record will be converted to an object in the format of {"b64": }, where is the Base64-encoded string of the content of the record. * For `file-list`, each file in the list will be converted to an object in the format of `{"b64": }`, where is the Base64-encoded string of the content of the file.
keyField This property is required. String
The name of the field that is considered as a key. The values identified by the key field is not included in the transformed instances that is sent to the Model. This is similar to specifying this name of the field in excluded_fields. In addition, the batch prediction output will not include the instances. Instead the output will only include the value of the key field, in a field named key in the output: * For jsonl output format, the output will have a key field instead of the instance field. * For csv/bigquery output format, the output will have have a key column instead of the instance feature columns. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
excludedFields This property is required. string[]
Fields that will be excluded in the prediction instance that is sent to the Model. Excluded will be attached to the batch prediction output if key_field is not specified. When excluded_fields is populated, included_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
includedFields This property is required. string[]
Fields that will be included in the prediction instance that is sent to the Model. If instance_type is array, the order of field names in included_fields also determines the order of the values in the array. When included_fields is populated, excluded_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
instanceType This property is required. string
The format of the instance that the Model accepts. Vertex AI will convert compatible batch prediction input instance formats to the specified format. Supported values are: * object: Each input is converted to JSON object format. * For bigquery, each row is converted to an object. * For jsonl, each line of the JSONL input must be an object. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. * array: Each input is converted to JSON array format. * For bigquery, each row is converted to an array. The order of columns is determined by the BigQuery column order, unless included_fields is populated. included_fields must be populated for specifying field orders. * For jsonl, if each line of the JSONL input is an object, included_fields must be populated for specifying field orders. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. If not specified, Vertex AI converts the batch prediction input as follows: * For bigquery and csv, the behavior is the same as array. The order of columns is the same as defined in the file or table, unless included_fields is populated. * For jsonl, the prediction instance format is determined by each line of the input. * For tf-record/tf-record-gzip, each record will be converted to an object in the format of {"b64": }, where is the Base64-encoded string of the content of the record. * For `file-list`, each file in the list will be converted to an object in the format of `{"b64": }`, where is the Base64-encoded string of the content of the file.
keyField This property is required. string
The name of the field that is considered as a key. The values identified by the key field is not included in the transformed instances that is sent to the Model. This is similar to specifying this name of the field in excluded_fields. In addition, the batch prediction output will not include the instances. Instead the output will only include the value of the key field, in a field named key in the output: * For jsonl output format, the output will have a key field instead of the instance field. * For csv/bigquery output format, the output will have have a key column instead of the instance feature columns. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
excluded_fields This property is required. Sequence[str]
Fields that will be excluded in the prediction instance that is sent to the Model. Excluded will be attached to the batch prediction output if key_field is not specified. When excluded_fields is populated, included_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
included_fields This property is required. Sequence[str]
Fields that will be included in the prediction instance that is sent to the Model. If instance_type is array, the order of field names in included_fields also determines the order of the values in the array. When included_fields is populated, excluded_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
instance_type This property is required. str
The format of the instance that the Model accepts. Vertex AI will convert compatible batch prediction input instance formats to the specified format. Supported values are: * object: Each input is converted to JSON object format. * For bigquery, each row is converted to an object. * For jsonl, each line of the JSONL input must be an object. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. * array: Each input is converted to JSON array format. * For bigquery, each row is converted to an array. The order of columns is determined by the BigQuery column order, unless included_fields is populated. included_fields must be populated for specifying field orders. * For jsonl, if each line of the JSONL input is an object, included_fields must be populated for specifying field orders. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. If not specified, Vertex AI converts the batch prediction input as follows: * For bigquery and csv, the behavior is the same as array. The order of columns is the same as defined in the file or table, unless included_fields is populated. * For jsonl, the prediction instance format is determined by each line of the input. * For tf-record/tf-record-gzip, each record will be converted to an object in the format of {"b64": }, where is the Base64-encoded string of the content of the record. * For `file-list`, each file in the list will be converted to an object in the format of `{"b64": }`, where is the Base64-encoded string of the content of the file.
key_field This property is required. str
The name of the field that is considered as a key. The values identified by the key field is not included in the transformed instances that is sent to the Model. This is similar to specifying this name of the field in excluded_fields. In addition, the batch prediction output will not include the instances. Instead the output will only include the value of the key field, in a field named key in the output: * For jsonl output format, the output will have a key field instead of the instance field. * For csv/bigquery output format, the output will have have a key column instead of the instance feature columns. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
excludedFields This property is required. List<String>
Fields that will be excluded in the prediction instance that is sent to the Model. Excluded will be attached to the batch prediction output if key_field is not specified. When excluded_fields is populated, included_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
includedFields This property is required. List<String>
Fields that will be included in the prediction instance that is sent to the Model. If instance_type is array, the order of field names in included_fields also determines the order of the values in the array. When included_fields is populated, excluded_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
instanceType This property is required. String
The format of the instance that the Model accepts. Vertex AI will convert compatible batch prediction input instance formats to the specified format. Supported values are: * object: Each input is converted to JSON object format. * For bigquery, each row is converted to an object. * For jsonl, each line of the JSONL input must be an object. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. * array: Each input is converted to JSON array format. * For bigquery, each row is converted to an array. The order of columns is determined by the BigQuery column order, unless included_fields is populated. included_fields must be populated for specifying field orders. * For jsonl, if each line of the JSONL input is an object, included_fields must be populated for specifying field orders. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. If not specified, Vertex AI converts the batch prediction input as follows: * For bigquery and csv, the behavior is the same as array. The order of columns is the same as defined in the file or table, unless included_fields is populated. * For jsonl, the prediction instance format is determined by each line of the input. * For tf-record/tf-record-gzip, each record will be converted to an object in the format of {"b64": }, where is the Base64-encoded string of the content of the record. * For `file-list`, each file in the list will be converted to an object in the format of `{"b64": }`, where is the Base64-encoded string of the content of the file.
keyField This property is required. String
The name of the field that is considered as a key. The values identified by the key field is not included in the transformed instances that is sent to the Model. This is similar to specifying this name of the field in excluded_fields. In addition, the batch prediction output will not include the instances. Instead the output will only include the value of the key field, in a field named key in the output: * For jsonl output format, the output will have a key field instead of the instance field. * For csv/bigquery output format, the output will have have a key column instead of the instance feature columns. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.

GoogleCloudAiplatformV1BatchPredictionJobOutputConfig
, GoogleCloudAiplatformV1BatchPredictionJobOutputConfigArgs

PredictionsFormat This property is required. string
The format in which Vertex AI gives the predictions, must be one of the Model's supported_output_storage_formats.
BigqueryDestination Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1BigQueryDestination
The BigQuery project or dataset location where the output is to be written to. If project is provided, a new dataset is created with name prediction__ where is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created, predictions, and errors. If the Model has both instance and prediction schemata defined then the tables have columns as follows: The predictions table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model's instance and prediction schemata. The errors table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single "errors" column, which as values has google.rpc.Status represented as a STRUCT, and containing only code and message.
GcsDestination Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1GcsDestination
The Cloud Storage location of the directory where the output is to be written to. In the given directory a new directory is created. Its name is prediction--, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files predictions_0001., predictions_0002., ..., predictions_N. are created where `` depends on chosen predictions_format, and N may equal 0001 and depends on the total number of successfully predicted instances. If the Model has both instance and prediction schemata defined then each such file contains predictions as per the predictions_format. If prediction for any instance failed (partially or completely), then an additional errors_0001., errors_0002.,..., errors_N. files are created (N depends on total number of failed predictions). These files contain the failed instances, as per their schema, followed by an additional error field which as value has google.rpc.Status containing only code and message fields.
PredictionsFormat This property is required. string
The format in which Vertex AI gives the predictions, must be one of the Model's supported_output_storage_formats.
BigqueryDestination GoogleCloudAiplatformV1BigQueryDestination
The BigQuery project or dataset location where the output is to be written to. If project is provided, a new dataset is created with name prediction__ where is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created, predictions, and errors. If the Model has both instance and prediction schemata defined then the tables have columns as follows: The predictions table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model's instance and prediction schemata. The errors table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single "errors" column, which as values has google.rpc.Status represented as a STRUCT, and containing only code and message.
GcsDestination GoogleCloudAiplatformV1GcsDestination
The Cloud Storage location of the directory where the output is to be written to. In the given directory a new directory is created. Its name is prediction--, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files predictions_0001., predictions_0002., ..., predictions_N. are created where `` depends on chosen predictions_format, and N may equal 0001 and depends on the total number of successfully predicted instances. If the Model has both instance and prediction schemata defined then each such file contains predictions as per the predictions_format. If prediction for any instance failed (partially or completely), then an additional errors_0001., errors_0002.,..., errors_N. files are created (N depends on total number of failed predictions). These files contain the failed instances, as per their schema, followed by an additional error field which as value has google.rpc.Status containing only code and message fields.
predictionsFormat This property is required. String
The format in which Vertex AI gives the predictions, must be one of the Model's supported_output_storage_formats.
bigqueryDestination GoogleCloudAiplatformV1BigQueryDestination
The BigQuery project or dataset location where the output is to be written to. If project is provided, a new dataset is created with name prediction__ where is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created, predictions, and errors. If the Model has both instance and prediction schemata defined then the tables have columns as follows: The predictions table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model's instance and prediction schemata. The errors table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single "errors" column, which as values has google.rpc.Status represented as a STRUCT, and containing only code and message.
gcsDestination GoogleCloudAiplatformV1GcsDestination
The Cloud Storage location of the directory where the output is to be written to. In the given directory a new directory is created. Its name is prediction--, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files predictions_0001., predictions_0002., ..., predictions_N. are created where `` depends on chosen predictions_format, and N may equal 0001 and depends on the total number of successfully predicted instances. If the Model has both instance and prediction schemata defined then each such file contains predictions as per the predictions_format. If prediction for any instance failed (partially or completely), then an additional errors_0001., errors_0002.,..., errors_N. files are created (N depends on total number of failed predictions). These files contain the failed instances, as per their schema, followed by an additional error field which as value has google.rpc.Status containing only code and message fields.
predictionsFormat This property is required. string
The format in which Vertex AI gives the predictions, must be one of the Model's supported_output_storage_formats.
bigqueryDestination GoogleCloudAiplatformV1BigQueryDestination
The BigQuery project or dataset location where the output is to be written to. If project is provided, a new dataset is created with name prediction__ where is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created, predictions, and errors. If the Model has both instance and prediction schemata defined then the tables have columns as follows: The predictions table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model's instance and prediction schemata. The errors table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single "errors" column, which as values has google.rpc.Status represented as a STRUCT, and containing only code and message.
gcsDestination GoogleCloudAiplatformV1GcsDestination
The Cloud Storage location of the directory where the output is to be written to. In the given directory a new directory is created. Its name is prediction--, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files predictions_0001., predictions_0002., ..., predictions_N. are created where `` depends on chosen predictions_format, and N may equal 0001 and depends on the total number of successfully predicted instances. If the Model has both instance and prediction schemata defined then each such file contains predictions as per the predictions_format. If prediction for any instance failed (partially or completely), then an additional errors_0001., errors_0002.,..., errors_N. files are created (N depends on total number of failed predictions). These files contain the failed instances, as per their schema, followed by an additional error field which as value has google.rpc.Status containing only code and message fields.
predictions_format This property is required. str
The format in which Vertex AI gives the predictions, must be one of the Model's supported_output_storage_formats.
bigquery_destination GoogleCloudAiplatformV1BigQueryDestination
The BigQuery project or dataset location where the output is to be written to. If project is provided, a new dataset is created with name prediction__ where is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created, predictions, and errors. If the Model has both instance and prediction schemata defined then the tables have columns as follows: The predictions table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model's instance and prediction schemata. The errors table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single "errors" column, which as values has google.rpc.Status represented as a STRUCT, and containing only code and message.
gcs_destination GoogleCloudAiplatformV1GcsDestination
The Cloud Storage location of the directory where the output is to be written to. In the given directory a new directory is created. Its name is prediction--, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files predictions_0001., predictions_0002., ..., predictions_N. are created where `` depends on chosen predictions_format, and N may equal 0001 and depends on the total number of successfully predicted instances. If the Model has both instance and prediction schemata defined then each such file contains predictions as per the predictions_format. If prediction for any instance failed (partially or completely), then an additional errors_0001., errors_0002.,..., errors_N. files are created (N depends on total number of failed predictions). These files contain the failed instances, as per their schema, followed by an additional error field which as value has google.rpc.Status containing only code and message fields.
predictionsFormat This property is required. String
The format in which Vertex AI gives the predictions, must be one of the Model's supported_output_storage_formats.
bigqueryDestination Property Map
The BigQuery project or dataset location where the output is to be written to. If project is provided, a new dataset is created with name prediction__ where is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created, predictions, and errors. If the Model has both instance and prediction schemata defined then the tables have columns as follows: The predictions table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model's instance and prediction schemata. The errors table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single "errors" column, which as values has google.rpc.Status represented as a STRUCT, and containing only code and message.
gcsDestination Property Map
The Cloud Storage location of the directory where the output is to be written to. In the given directory a new directory is created. Its name is prediction--, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files predictions_0001., predictions_0002., ..., predictions_N. are created where `` depends on chosen predictions_format, and N may equal 0001 and depends on the total number of successfully predicted instances. If the Model has both instance and prediction schemata defined then each such file contains predictions as per the predictions_format. If prediction for any instance failed (partially or completely), then an additional errors_0001., errors_0002.,..., errors_N. files are created (N depends on total number of failed predictions). These files contain the failed instances, as per their schema, followed by an additional error field which as value has google.rpc.Status containing only code and message fields.

GoogleCloudAiplatformV1BatchPredictionJobOutputConfigResponse
, GoogleCloudAiplatformV1BatchPredictionJobOutputConfigResponseArgs

BigqueryDestination This property is required. Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1BigQueryDestinationResponse
The BigQuery project or dataset location where the output is to be written to. If project is provided, a new dataset is created with name prediction__ where is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created, predictions, and errors. If the Model has both instance and prediction schemata defined then the tables have columns as follows: The predictions table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model's instance and prediction schemata. The errors table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single "errors" column, which as values has google.rpc.Status represented as a STRUCT, and containing only code and message.
GcsDestination This property is required. Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1GcsDestinationResponse
The Cloud Storage location of the directory where the output is to be written to. In the given directory a new directory is created. Its name is prediction--, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files predictions_0001., predictions_0002., ..., predictions_N. are created where `` depends on chosen predictions_format, and N may equal 0001 and depends on the total number of successfully predicted instances. If the Model has both instance and prediction schemata defined then each such file contains predictions as per the predictions_format. If prediction for any instance failed (partially or completely), then an additional errors_0001., errors_0002.,..., errors_N. files are created (N depends on total number of failed predictions). These files contain the failed instances, as per their schema, followed by an additional error field which as value has google.rpc.Status containing only code and message fields.
PredictionsFormat This property is required. string
The format in which Vertex AI gives the predictions, must be one of the Model's supported_output_storage_formats.
BigqueryDestination This property is required. GoogleCloudAiplatformV1BigQueryDestinationResponse
The BigQuery project or dataset location where the output is to be written to. If project is provided, a new dataset is created with name prediction__ where is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created, predictions, and errors. If the Model has both instance and prediction schemata defined then the tables have columns as follows: The predictions table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model's instance and prediction schemata. The errors table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single "errors" column, which as values has google.rpc.Status represented as a STRUCT, and containing only code and message.
GcsDestination This property is required. GoogleCloudAiplatformV1GcsDestinationResponse
The Cloud Storage location of the directory where the output is to be written to. In the given directory a new directory is created. Its name is prediction--, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files predictions_0001., predictions_0002., ..., predictions_N. are created where `` depends on chosen predictions_format, and N may equal 0001 and depends on the total number of successfully predicted instances. If the Model has both instance and prediction schemata defined then each such file contains predictions as per the predictions_format. If prediction for any instance failed (partially or completely), then an additional errors_0001., errors_0002.,..., errors_N. files are created (N depends on total number of failed predictions). These files contain the failed instances, as per their schema, followed by an additional error field which as value has google.rpc.Status containing only code and message fields.
PredictionsFormat This property is required. string
The format in which Vertex AI gives the predictions, must be one of the Model's supported_output_storage_formats.
bigqueryDestination This property is required. GoogleCloudAiplatformV1BigQueryDestinationResponse
The BigQuery project or dataset location where the output is to be written to. If project is provided, a new dataset is created with name prediction__ where is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created, predictions, and errors. If the Model has both instance and prediction schemata defined then the tables have columns as follows: The predictions table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model's instance and prediction schemata. The errors table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single "errors" column, which as values has google.rpc.Status represented as a STRUCT, and containing only code and message.
gcsDestination This property is required. GoogleCloudAiplatformV1GcsDestinationResponse
The Cloud Storage location of the directory where the output is to be written to. In the given directory a new directory is created. Its name is prediction--, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files predictions_0001., predictions_0002., ..., predictions_N. are created where `` depends on chosen predictions_format, and N may equal 0001 and depends on the total number of successfully predicted instances. If the Model has both instance and prediction schemata defined then each such file contains predictions as per the predictions_format. If prediction for any instance failed (partially or completely), then an additional errors_0001., errors_0002.,..., errors_N. files are created (N depends on total number of failed predictions). These files contain the failed instances, as per their schema, followed by an additional error field which as value has google.rpc.Status containing only code and message fields.
predictionsFormat This property is required. String
The format in which Vertex AI gives the predictions, must be one of the Model's supported_output_storage_formats.
bigqueryDestination This property is required. GoogleCloudAiplatformV1BigQueryDestinationResponse
The BigQuery project or dataset location where the output is to be written to. If project is provided, a new dataset is created with name prediction__ where is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created, predictions, and errors. If the Model has both instance and prediction schemata defined then the tables have columns as follows: The predictions table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model's instance and prediction schemata. The errors table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single "errors" column, which as values has google.rpc.Status represented as a STRUCT, and containing only code and message.
gcsDestination This property is required. GoogleCloudAiplatformV1GcsDestinationResponse
The Cloud Storage location of the directory where the output is to be written to. In the given directory a new directory is created. Its name is prediction--, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files predictions_0001., predictions_0002., ..., predictions_N. are created where `` depends on chosen predictions_format, and N may equal 0001 and depends on the total number of successfully predicted instances. If the Model has both instance and prediction schemata defined then each such file contains predictions as per the predictions_format. If prediction for any instance failed (partially or completely), then an additional errors_0001., errors_0002.,..., errors_N. files are created (N depends on total number of failed predictions). These files contain the failed instances, as per their schema, followed by an additional error field which as value has google.rpc.Status containing only code and message fields.
predictionsFormat This property is required. string
The format in which Vertex AI gives the predictions, must be one of the Model's supported_output_storage_formats.
bigquery_destination This property is required. GoogleCloudAiplatformV1BigQueryDestinationResponse
The BigQuery project or dataset location where the output is to be written to. If project is provided, a new dataset is created with name prediction__ where is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created, predictions, and errors. If the Model has both instance and prediction schemata defined then the tables have columns as follows: The predictions table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model's instance and prediction schemata. The errors table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single "errors" column, which as values has google.rpc.Status represented as a STRUCT, and containing only code and message.
gcs_destination This property is required. GoogleCloudAiplatformV1GcsDestinationResponse
The Cloud Storage location of the directory where the output is to be written to. In the given directory a new directory is created. Its name is prediction--, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files predictions_0001., predictions_0002., ..., predictions_N. are created where `` depends on chosen predictions_format, and N may equal 0001 and depends on the total number of successfully predicted instances. If the Model has both instance and prediction schemata defined then each such file contains predictions as per the predictions_format. If prediction for any instance failed (partially or completely), then an additional errors_0001., errors_0002.,..., errors_N. files are created (N depends on total number of failed predictions). These files contain the failed instances, as per their schema, followed by an additional error field which as value has google.rpc.Status containing only code and message fields.
predictions_format This property is required. str
The format in which Vertex AI gives the predictions, must be one of the Model's supported_output_storage_formats.
bigqueryDestination This property is required. Property Map
The BigQuery project or dataset location where the output is to be written to. If project is provided, a new dataset is created with name prediction__ where is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created, predictions, and errors. If the Model has both instance and prediction schemata defined then the tables have columns as follows: The predictions table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model's instance and prediction schemata. The errors table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single "errors" column, which as values has google.rpc.Status represented as a STRUCT, and containing only code and message.
gcsDestination This property is required. Property Map
The Cloud Storage location of the directory where the output is to be written to. In the given directory a new directory is created. Its name is prediction--, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files predictions_0001., predictions_0002., ..., predictions_N. are created where `` depends on chosen predictions_format, and N may equal 0001 and depends on the total number of successfully predicted instances. If the Model has both instance and prediction schemata defined then each such file contains predictions as per the predictions_format. If prediction for any instance failed (partially or completely), then an additional errors_0001., errors_0002.,..., errors_N. files are created (N depends on total number of failed predictions). These files contain the failed instances, as per their schema, followed by an additional error field which as value has google.rpc.Status containing only code and message fields.
predictionsFormat This property is required. String
The format in which Vertex AI gives the predictions, must be one of the Model's supported_output_storage_formats.

GoogleCloudAiplatformV1BatchPredictionJobOutputInfoResponse
, GoogleCloudAiplatformV1BatchPredictionJobOutputInfoResponseArgs

BigqueryOutputDataset This property is required. string
The path of the BigQuery dataset created, in bq://projectId.bqDatasetId format, into which the prediction output is written.
BigqueryOutputTable This property is required. string
The name of the BigQuery table created, in predictions_ format, into which the prediction output is written. Can be used by UI to generate the BigQuery output path, for example.
GcsOutputDirectory This property is required. string
The full path of the Cloud Storage directory created, into which the prediction output is written.
BigqueryOutputDataset This property is required. string
The path of the BigQuery dataset created, in bq://projectId.bqDatasetId format, into which the prediction output is written.
BigqueryOutputTable This property is required. string
The name of the BigQuery table created, in predictions_ format, into which the prediction output is written. Can be used by UI to generate the BigQuery output path, for example.
GcsOutputDirectory This property is required. string
The full path of the Cloud Storage directory created, into which the prediction output is written.
bigqueryOutputDataset This property is required. String
The path of the BigQuery dataset created, in bq://projectId.bqDatasetId format, into which the prediction output is written.
bigqueryOutputTable This property is required. String
The name of the BigQuery table created, in predictions_ format, into which the prediction output is written. Can be used by UI to generate the BigQuery output path, for example.
gcsOutputDirectory This property is required. String
The full path of the Cloud Storage directory created, into which the prediction output is written.
bigqueryOutputDataset This property is required. string
The path of the BigQuery dataset created, in bq://projectId.bqDatasetId format, into which the prediction output is written.
bigqueryOutputTable This property is required. string
The name of the BigQuery table created, in predictions_ format, into which the prediction output is written. Can be used by UI to generate the BigQuery output path, for example.
gcsOutputDirectory This property is required. string
The full path of the Cloud Storage directory created, into which the prediction output is written.
bigquery_output_dataset This property is required. str
The path of the BigQuery dataset created, in bq://projectId.bqDatasetId format, into which the prediction output is written.
bigquery_output_table This property is required. str
The name of the BigQuery table created, in predictions_ format, into which the prediction output is written. Can be used by UI to generate the BigQuery output path, for example.
gcs_output_directory This property is required. str
The full path of the Cloud Storage directory created, into which the prediction output is written.
bigqueryOutputDataset This property is required. String
The path of the BigQuery dataset created, in bq://projectId.bqDatasetId format, into which the prediction output is written.
bigqueryOutputTable This property is required. String
The name of the BigQuery table created, in predictions_ format, into which the prediction output is written. Can be used by UI to generate the BigQuery output path, for example.
gcsOutputDirectory This property is required. String
The full path of the Cloud Storage directory created, into which the prediction output is written.

GoogleCloudAiplatformV1BigQueryDestination
, GoogleCloudAiplatformV1BigQueryDestinationArgs

OutputUri This property is required. string
BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: * BigQuery path. For example: bq://projectId or bq://projectId.bqDatasetId or bq://projectId.bqDatasetId.bqTableId.
OutputUri This property is required. string
BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: * BigQuery path. For example: bq://projectId or bq://projectId.bqDatasetId or bq://projectId.bqDatasetId.bqTableId.
outputUri This property is required. String
BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: * BigQuery path. For example: bq://projectId or bq://projectId.bqDatasetId or bq://projectId.bqDatasetId.bqTableId.
outputUri This property is required. string
BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: * BigQuery path. For example: bq://projectId or bq://projectId.bqDatasetId or bq://projectId.bqDatasetId.bqTableId.
output_uri This property is required. str
BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: * BigQuery path. For example: bq://projectId or bq://projectId.bqDatasetId or bq://projectId.bqDatasetId.bqTableId.
outputUri This property is required. String
BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: * BigQuery path. For example: bq://projectId or bq://projectId.bqDatasetId or bq://projectId.bqDatasetId.bqTableId.

GoogleCloudAiplatformV1BigQueryDestinationResponse
, GoogleCloudAiplatformV1BigQueryDestinationResponseArgs

OutputUri This property is required. string
BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: * BigQuery path. For example: bq://projectId or bq://projectId.bqDatasetId or bq://projectId.bqDatasetId.bqTableId.
OutputUri This property is required. string
BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: * BigQuery path. For example: bq://projectId or bq://projectId.bqDatasetId or bq://projectId.bqDatasetId.bqTableId.
outputUri This property is required. String
BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: * BigQuery path. For example: bq://projectId or bq://projectId.bqDatasetId or bq://projectId.bqDatasetId.bqTableId.
outputUri This property is required. string
BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: * BigQuery path. For example: bq://projectId or bq://projectId.bqDatasetId or bq://projectId.bqDatasetId.bqTableId.
output_uri This property is required. str
BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: * BigQuery path. For example: bq://projectId or bq://projectId.bqDatasetId or bq://projectId.bqDatasetId.bqTableId.
outputUri This property is required. String
BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: * BigQuery path. For example: bq://projectId or bq://projectId.bqDatasetId or bq://projectId.bqDatasetId.bqTableId.

GoogleCloudAiplatformV1BigQuerySource
, GoogleCloudAiplatformV1BigQuerySourceArgs

InputUri This property is required. string
BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: bq://projectId.bqDatasetId.bqTableId.
InputUri This property is required. string
BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: bq://projectId.bqDatasetId.bqTableId.
inputUri This property is required. String
BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: bq://projectId.bqDatasetId.bqTableId.
inputUri This property is required. string
BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: bq://projectId.bqDatasetId.bqTableId.
input_uri This property is required. str
BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: bq://projectId.bqDatasetId.bqTableId.
inputUri This property is required. String
BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: bq://projectId.bqDatasetId.bqTableId.

GoogleCloudAiplatformV1BigQuerySourceResponse
, GoogleCloudAiplatformV1BigQuerySourceResponseArgs

InputUri This property is required. string
BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: bq://projectId.bqDatasetId.bqTableId.
InputUri This property is required. string
BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: bq://projectId.bqDatasetId.bqTableId.
inputUri This property is required. String
BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: bq://projectId.bqDatasetId.bqTableId.
inputUri This property is required. string
BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: bq://projectId.bqDatasetId.bqTableId.
input_uri This property is required. str
BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: bq://projectId.bqDatasetId.bqTableId.
inputUri This property is required. String
BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: bq://projectId.bqDatasetId.bqTableId.

GoogleCloudAiplatformV1BlurBaselineConfig
, GoogleCloudAiplatformV1BlurBaselineConfigArgs

MaxBlurSigma double
The standard deviation of the blur kernel for the blurred baseline. The same blurring parameter is used for both the height and the width dimension. If not set, the method defaults to the zero (i.e. black for images) baseline.
MaxBlurSigma float64
The standard deviation of the blur kernel for the blurred baseline. The same blurring parameter is used for both the height and the width dimension. If not set, the method defaults to the zero (i.e. black for images) baseline.
maxBlurSigma Double
The standard deviation of the blur kernel for the blurred baseline. The same blurring parameter is used for both the height and the width dimension. If not set, the method defaults to the zero (i.e. black for images) baseline.
maxBlurSigma number
The standard deviation of the blur kernel for the blurred baseline. The same blurring parameter is used for both the height and the width dimension. If not set, the method defaults to the zero (i.e. black for images) baseline.
max_blur_sigma float
The standard deviation of the blur kernel for the blurred baseline. The same blurring parameter is used for both the height and the width dimension. If not set, the method defaults to the zero (i.e. black for images) baseline.
maxBlurSigma Number
The standard deviation of the blur kernel for the blurred baseline. The same blurring parameter is used for both the height and the width dimension. If not set, the method defaults to the zero (i.e. black for images) baseline.

GoogleCloudAiplatformV1BlurBaselineConfigResponse
, GoogleCloudAiplatformV1BlurBaselineConfigResponseArgs

MaxBlurSigma This property is required. double
The standard deviation of the blur kernel for the blurred baseline. The same blurring parameter is used for both the height and the width dimension. If not set, the method defaults to the zero (i.e. black for images) baseline.
MaxBlurSigma This property is required. float64
The standard deviation of the blur kernel for the blurred baseline. The same blurring parameter is used for both the height and the width dimension. If not set, the method defaults to the zero (i.e. black for images) baseline.
maxBlurSigma This property is required. Double
The standard deviation of the blur kernel for the blurred baseline. The same blurring parameter is used for both the height and the width dimension. If not set, the method defaults to the zero (i.e. black for images) baseline.
maxBlurSigma This property is required. number
The standard deviation of the blur kernel for the blurred baseline. The same blurring parameter is used for both the height and the width dimension. If not set, the method defaults to the zero (i.e. black for images) baseline.
max_blur_sigma This property is required. float
The standard deviation of the blur kernel for the blurred baseline. The same blurring parameter is used for both the height and the width dimension. If not set, the method defaults to the zero (i.e. black for images) baseline.
maxBlurSigma This property is required. Number
The standard deviation of the blur kernel for the blurred baseline. The same blurring parameter is used for both the height and the width dimension. If not set, the method defaults to the zero (i.e. black for images) baseline.

GoogleCloudAiplatformV1CompletionStatsResponse
, GoogleCloudAiplatformV1CompletionStatsResponseArgs

FailedCount This property is required. string
The number of entities for which any error was encountered.
IncompleteCount This property is required. string
In cases when enough errors are encountered a job, pipeline, or operation may be failed as a whole. Below is the number of entities for which the processing had not been finished (either in successful or failed state). Set to -1 if the number is unknown (for example, the operation failed before the total entity number could be collected).
SuccessfulCount This property is required. string
The number of entities that had been processed successfully.
SuccessfulForecastPointCount This property is required. string
The number of the successful forecast points that are generated by the forecasting model. This is ONLY used by the forecasting batch prediction.
FailedCount This property is required. string
The number of entities for which any error was encountered.
IncompleteCount This property is required. string
In cases when enough errors are encountered a job, pipeline, or operation may be failed as a whole. Below is the number of entities for which the processing had not been finished (either in successful or failed state). Set to -1 if the number is unknown (for example, the operation failed before the total entity number could be collected).
SuccessfulCount This property is required. string
The number of entities that had been processed successfully.
SuccessfulForecastPointCount This property is required. string
The number of the successful forecast points that are generated by the forecasting model. This is ONLY used by the forecasting batch prediction.
failedCount This property is required. String
The number of entities for which any error was encountered.
incompleteCount This property is required. String
In cases when enough errors are encountered a job, pipeline, or operation may be failed as a whole. Below is the number of entities for which the processing had not been finished (either in successful or failed state). Set to -1 if the number is unknown (for example, the operation failed before the total entity number could be collected).
successfulCount This property is required. String
The number of entities that had been processed successfully.
successfulForecastPointCount This property is required. String
The number of the successful forecast points that are generated by the forecasting model. This is ONLY used by the forecasting batch prediction.
failedCount This property is required. string
The number of entities for which any error was encountered.
incompleteCount This property is required. string
In cases when enough errors are encountered a job, pipeline, or operation may be failed as a whole. Below is the number of entities for which the processing had not been finished (either in successful or failed state). Set to -1 if the number is unknown (for example, the operation failed before the total entity number could be collected).
successfulCount This property is required. string
The number of entities that had been processed successfully.
successfulForecastPointCount This property is required. string
The number of the successful forecast points that are generated by the forecasting model. This is ONLY used by the forecasting batch prediction.
failed_count This property is required. str
The number of entities for which any error was encountered.
incomplete_count This property is required. str
In cases when enough errors are encountered a job, pipeline, or operation may be failed as a whole. Below is the number of entities for which the processing had not been finished (either in successful or failed state). Set to -1 if the number is unknown (for example, the operation failed before the total entity number could be collected).
successful_count This property is required. str
The number of entities that had been processed successfully.
successful_forecast_point_count This property is required. str
The number of the successful forecast points that are generated by the forecasting model. This is ONLY used by the forecasting batch prediction.
failedCount This property is required. String
The number of entities for which any error was encountered.
incompleteCount This property is required. String
In cases when enough errors are encountered a job, pipeline, or operation may be failed as a whole. Below is the number of entities for which the processing had not been finished (either in successful or failed state). Set to -1 if the number is unknown (for example, the operation failed before the total entity number could be collected).
successfulCount This property is required. String
The number of entities that had been processed successfully.
successfulForecastPointCount This property is required. String
The number of the successful forecast points that are generated by the forecasting model. This is ONLY used by the forecasting batch prediction.

GoogleCloudAiplatformV1EncryptionSpec
, GoogleCloudAiplatformV1EncryptionSpecArgs

KmsKeyName This property is required. string
The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.
KmsKeyName This property is required. string
The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.
kmsKeyName This property is required. String
The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.
kmsKeyName This property is required. string
The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.
kms_key_name This property is required. str
The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.
kmsKeyName This property is required. String
The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.

GoogleCloudAiplatformV1EncryptionSpecResponse
, GoogleCloudAiplatformV1EncryptionSpecResponseArgs

KmsKeyName This property is required. string
The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.
KmsKeyName This property is required. string
The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.
kmsKeyName This property is required. String
The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.
kmsKeyName This property is required. string
The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.
kms_key_name This property is required. str
The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.
kmsKeyName This property is required. String
The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.

GoogleCloudAiplatformV1EnvVar
, GoogleCloudAiplatformV1EnvVarArgs

Name This property is required. string
Name of the environment variable. Must be a valid C identifier.
Value This property is required. string
Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
Name This property is required. string
Name of the environment variable. Must be a valid C identifier.
Value This property is required. string
Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
name This property is required. String
Name of the environment variable. Must be a valid C identifier.
value This property is required. String
Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
name This property is required. string
Name of the environment variable. Must be a valid C identifier.
value This property is required. string
Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
name This property is required. str
Name of the environment variable. Must be a valid C identifier.
value This property is required. str
Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
name This property is required. String
Name of the environment variable. Must be a valid C identifier.
value This property is required. String
Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.

GoogleCloudAiplatformV1EnvVarResponse
, GoogleCloudAiplatformV1EnvVarResponseArgs

Name This property is required. string
Name of the environment variable. Must be a valid C identifier.
Value This property is required. string
Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
Name This property is required. string
Name of the environment variable. Must be a valid C identifier.
Value This property is required. string
Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
name This property is required. String
Name of the environment variable. Must be a valid C identifier.
value This property is required. String
Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
name This property is required. string
Name of the environment variable. Must be a valid C identifier.
value This property is required. string
Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
name This property is required. str
Name of the environment variable. Must be a valid C identifier.
value This property is required. str
Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
name This property is required. String
Name of the environment variable. Must be a valid C identifier.
value This property is required. String
Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.

GoogleCloudAiplatformV1Examples
, GoogleCloudAiplatformV1ExamplesArgs

ExampleGcsSource Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ExamplesExampleGcsSource
The Cloud Storage input instances.
NearestNeighborSearchConfig object
The full configuration for the generated index, the semantics are the same as metadata and should match NearestNeighborSearchConfig.
NeighborCount int
The number of neighbors to return when querying for examples.
Presets Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1Presets
Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
ExampleGcsSource GoogleCloudAiplatformV1ExamplesExampleGcsSource
The Cloud Storage input instances.
NearestNeighborSearchConfig interface{}
The full configuration for the generated index, the semantics are the same as metadata and should match NearestNeighborSearchConfig.
NeighborCount int
The number of neighbors to return when querying for examples.
Presets GoogleCloudAiplatformV1Presets
Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
exampleGcsSource GoogleCloudAiplatformV1ExamplesExampleGcsSource
The Cloud Storage input instances.
nearestNeighborSearchConfig Object
The full configuration for the generated index, the semantics are the same as metadata and should match NearestNeighborSearchConfig.
neighborCount Integer
The number of neighbors to return when querying for examples.
presets GoogleCloudAiplatformV1Presets
Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
exampleGcsSource GoogleCloudAiplatformV1ExamplesExampleGcsSource
The Cloud Storage input instances.
nearestNeighborSearchConfig any
The full configuration for the generated index, the semantics are the same as metadata and should match NearestNeighborSearchConfig.
neighborCount number
The number of neighbors to return when querying for examples.
presets GoogleCloudAiplatformV1Presets
Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
example_gcs_source GoogleCloudAiplatformV1ExamplesExampleGcsSource
The Cloud Storage input instances.
nearest_neighbor_search_config Any
The full configuration for the generated index, the semantics are the same as metadata and should match NearestNeighborSearchConfig.
neighbor_count int
The number of neighbors to return when querying for examples.
presets GoogleCloudAiplatformV1Presets
Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
exampleGcsSource Property Map
The Cloud Storage input instances.
nearestNeighborSearchConfig Any
The full configuration for the generated index, the semantics are the same as metadata and should match NearestNeighborSearchConfig.
neighborCount Number
The number of neighbors to return when querying for examples.
presets Property Map
Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.

GoogleCloudAiplatformV1ExamplesExampleGcsSource
, GoogleCloudAiplatformV1ExamplesExampleGcsSourceArgs

DataFormat Pulumi.GoogleNative.Aiplatform.V1.GoogleCloudAiplatformV1ExamplesExampleGcsSourceDataFormat
The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
GcsSource Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1GcsSource
The Cloud Storage location for the input instances.
DataFormat GoogleCloudAiplatformV1ExamplesExampleGcsSourceDataFormat
The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
GcsSource GoogleCloudAiplatformV1GcsSource
The Cloud Storage location for the input instances.
dataFormat GoogleCloudAiplatformV1ExamplesExampleGcsSourceDataFormat
The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
gcsSource GoogleCloudAiplatformV1GcsSource
The Cloud Storage location for the input instances.
dataFormat GoogleCloudAiplatformV1ExamplesExampleGcsSourceDataFormat
The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
gcsSource GoogleCloudAiplatformV1GcsSource
The Cloud Storage location for the input instances.
data_format GoogleCloudAiplatformV1ExamplesExampleGcsSourceDataFormat
The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
gcs_source GoogleCloudAiplatformV1GcsSource
The Cloud Storage location for the input instances.
dataFormat "DATA_FORMAT_UNSPECIFIED" | "JSONL"
The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
gcsSource Property Map
The Cloud Storage location for the input instances.

GoogleCloudAiplatformV1ExamplesExampleGcsSourceDataFormat
, GoogleCloudAiplatformV1ExamplesExampleGcsSourceDataFormatArgs

DataFormatUnspecified
DATA_FORMAT_UNSPECIFIEDFormat unspecified, used when unset.
Jsonl
JSONLExamples are stored in JSONL files.
GoogleCloudAiplatformV1ExamplesExampleGcsSourceDataFormatDataFormatUnspecified
DATA_FORMAT_UNSPECIFIEDFormat unspecified, used when unset.
GoogleCloudAiplatformV1ExamplesExampleGcsSourceDataFormatJsonl
JSONLExamples are stored in JSONL files.
DataFormatUnspecified
DATA_FORMAT_UNSPECIFIEDFormat unspecified, used when unset.
Jsonl
JSONLExamples are stored in JSONL files.
DataFormatUnspecified
DATA_FORMAT_UNSPECIFIEDFormat unspecified, used when unset.
Jsonl
JSONLExamples are stored in JSONL files.
DATA_FORMAT_UNSPECIFIED
DATA_FORMAT_UNSPECIFIEDFormat unspecified, used when unset.
JSONL
JSONLExamples are stored in JSONL files.
"DATA_FORMAT_UNSPECIFIED"
DATA_FORMAT_UNSPECIFIEDFormat unspecified, used when unset.
"JSONL"
JSONLExamples are stored in JSONL files.

GoogleCloudAiplatformV1ExamplesExampleGcsSourceResponse
, GoogleCloudAiplatformV1ExamplesExampleGcsSourceResponseArgs

DataFormat This property is required. string
The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
GcsSource This property is required. Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1GcsSourceResponse
The Cloud Storage location for the input instances.
DataFormat This property is required. string
The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
GcsSource This property is required. GoogleCloudAiplatformV1GcsSourceResponse
The Cloud Storage location for the input instances.
dataFormat This property is required. String
The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
gcsSource This property is required. GoogleCloudAiplatformV1GcsSourceResponse
The Cloud Storage location for the input instances.
dataFormat This property is required. string
The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
gcsSource This property is required. GoogleCloudAiplatformV1GcsSourceResponse
The Cloud Storage location for the input instances.
data_format This property is required. str
The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
gcs_source This property is required. GoogleCloudAiplatformV1GcsSourceResponse
The Cloud Storage location for the input instances.
dataFormat This property is required. String
The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
gcsSource This property is required. Property Map
The Cloud Storage location for the input instances.

GoogleCloudAiplatformV1ExamplesResponse
, GoogleCloudAiplatformV1ExamplesResponseArgs

ExampleGcsSource This property is required. Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ExamplesExampleGcsSourceResponse
The Cloud Storage input instances.
NearestNeighborSearchConfig This property is required. object
The full configuration for the generated index, the semantics are the same as metadata and should match NearestNeighborSearchConfig.
NeighborCount This property is required. int
The number of neighbors to return when querying for examples.
Presets This property is required. Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1PresetsResponse
Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
ExampleGcsSource This property is required. GoogleCloudAiplatformV1ExamplesExampleGcsSourceResponse
The Cloud Storage input instances.
NearestNeighborSearchConfig This property is required. interface{}
The full configuration for the generated index, the semantics are the same as metadata and should match NearestNeighborSearchConfig.
NeighborCount This property is required. int
The number of neighbors to return when querying for examples.
Presets This property is required. GoogleCloudAiplatformV1PresetsResponse
Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
exampleGcsSource This property is required. GoogleCloudAiplatformV1ExamplesExampleGcsSourceResponse
The Cloud Storage input instances.
nearestNeighborSearchConfig This property is required. Object
The full configuration for the generated index, the semantics are the same as metadata and should match NearestNeighborSearchConfig.
neighborCount This property is required. Integer
The number of neighbors to return when querying for examples.
presets This property is required. GoogleCloudAiplatformV1PresetsResponse
Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
exampleGcsSource This property is required. GoogleCloudAiplatformV1ExamplesExampleGcsSourceResponse
The Cloud Storage input instances.
nearestNeighborSearchConfig This property is required. any
The full configuration for the generated index, the semantics are the same as metadata and should match NearestNeighborSearchConfig.
neighborCount This property is required. number
The number of neighbors to return when querying for examples.
presets This property is required. GoogleCloudAiplatformV1PresetsResponse
Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
example_gcs_source This property is required. GoogleCloudAiplatformV1ExamplesExampleGcsSourceResponse
The Cloud Storage input instances.
nearest_neighbor_search_config This property is required. Any
The full configuration for the generated index, the semantics are the same as metadata and should match NearestNeighborSearchConfig.
neighbor_count This property is required. int
The number of neighbors to return when querying for examples.
presets This property is required. GoogleCloudAiplatformV1PresetsResponse
Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
exampleGcsSource This property is required. Property Map
The Cloud Storage input instances.
nearestNeighborSearchConfig This property is required. Any
The full configuration for the generated index, the semantics are the same as metadata and should match NearestNeighborSearchConfig.
neighborCount This property is required. Number
The number of neighbors to return when querying for examples.
presets This property is required. Property Map
Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.

GoogleCloudAiplatformV1ExplanationMetadata
, GoogleCloudAiplatformV1ExplanationMetadataArgs

Inputs This property is required. Dictionary<string, string>
Map from feature names to feature input metadata. Keys are the name of the features. Values are the specification of the feature. An empty InputMetadata is valid. It describes a text feature which has the name specified as the key in ExplanationMetadata.inputs. The baseline of the empty feature is chosen by Vertex AI. For Vertex AI-provided Tensorflow images, the key can be any friendly name of the feature. Once specified, featureAttributions are keyed by this key (if not grouped with another feature). For custom images, the key must match with the key in instance.
Outputs This property is required. Dictionary<string, string>
Map from output names to output metadata. For Vertex AI-provided Tensorflow images, keys can be any user defined string that consists of any UTF-8 characters. For custom images, keys are the name of the output field in the prediction to be explained. Currently only one key is allowed.
FeatureAttributionsSchemaUri string
Points to a YAML file stored on Google Cloud Storage describing the format of the feature attributions. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML tabular Models always have this field populated by Vertex AI. Note: The URI given on output may be different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
LatentSpaceSource string
Name of the source to generate embeddings for example based explanations.
Inputs This property is required. map[string]string
Map from feature names to feature input metadata. Keys are the name of the features. Values are the specification of the feature. An empty InputMetadata is valid. It describes a text feature which has the name specified as the key in ExplanationMetadata.inputs. The baseline of the empty feature is chosen by Vertex AI. For Vertex AI-provided Tensorflow images, the key can be any friendly name of the feature. Once specified, featureAttributions are keyed by this key (if not grouped with another feature). For custom images, the key must match with the key in instance.
Outputs This property is required. map[string]string
Map from output names to output metadata. For Vertex AI-provided Tensorflow images, keys can be any user defined string that consists of any UTF-8 characters. For custom images, keys are the name of the output field in the prediction to be explained. Currently only one key is allowed.
FeatureAttributionsSchemaUri string
Points to a YAML file stored on Google Cloud Storage describing the format of the feature attributions. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML tabular Models always have this field populated by Vertex AI. Note: The URI given on output may be different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
LatentSpaceSource string
Name of the source to generate embeddings for example based explanations.
inputs This property is required. Map<String,String>
Map from feature names to feature input metadata. Keys are the name of the features. Values are the specification of the feature. An empty InputMetadata is valid. It describes a text feature which has the name specified as the key in ExplanationMetadata.inputs. The baseline of the empty feature is chosen by Vertex AI. For Vertex AI-provided Tensorflow images, the key can be any friendly name of the feature. Once specified, featureAttributions are keyed by this key (if not grouped with another feature). For custom images, the key must match with the key in instance.
outputs This property is required. Map<String,String>
Map from output names to output metadata. For Vertex AI-provided Tensorflow images, keys can be any user defined string that consists of any UTF-8 characters. For custom images, keys are the name of the output field in the prediction to be explained. Currently only one key is allowed.
featureAttributionsSchemaUri String
Points to a YAML file stored on Google Cloud Storage describing the format of the feature attributions. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML tabular Models always have this field populated by Vertex AI. Note: The URI given on output may be different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
latentSpaceSource String
Name of the source to generate embeddings for example based explanations.
inputs This property is required. {[key: string]: string}
Map from feature names to feature input metadata. Keys are the name of the features. Values are the specification of the feature. An empty InputMetadata is valid. It describes a text feature which has the name specified as the key in ExplanationMetadata.inputs. The baseline of the empty feature is chosen by Vertex AI. For Vertex AI-provided Tensorflow images, the key can be any friendly name of the feature. Once specified, featureAttributions are keyed by this key (if not grouped with another feature). For custom images, the key must match with the key in instance.
outputs This property is required. {[key: string]: string}
Map from output names to output metadata. For Vertex AI-provided Tensorflow images, keys can be any user defined string that consists of any UTF-8 characters. For custom images, keys are the name of the output field in the prediction to be explained. Currently only one key is allowed.
featureAttributionsSchemaUri string
Points to a YAML file stored on Google Cloud Storage describing the format of the feature attributions. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML tabular Models always have this field populated by Vertex AI. Note: The URI given on output may be different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
latentSpaceSource string
Name of the source to generate embeddings for example based explanations.
inputs This property is required. Mapping[str, str]
Map from feature names to feature input metadata. Keys are the name of the features. Values are the specification of the feature. An empty InputMetadata is valid. It describes a text feature which has the name specified as the key in ExplanationMetadata.inputs. The baseline of the empty feature is chosen by Vertex AI. For Vertex AI-provided Tensorflow images, the key can be any friendly name of the feature. Once specified, featureAttributions are keyed by this key (if not grouped with another feature). For custom images, the key must match with the key in instance.
outputs This property is required. Mapping[str, str]
Map from output names to output metadata. For Vertex AI-provided Tensorflow images, keys can be any user defined string that consists of any UTF-8 characters. For custom images, keys are the name of the output field in the prediction to be explained. Currently only one key is allowed.
feature_attributions_schema_uri str
Points to a YAML file stored on Google Cloud Storage describing the format of the feature attributions. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML tabular Models always have this field populated by Vertex AI. Note: The URI given on output may be different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
latent_space_source str
Name of the source to generate embeddings for example based explanations.
inputs This property is required. Map<String>
Map from feature names to feature input metadata. Keys are the name of the features. Values are the specification of the feature. An empty InputMetadata is valid. It describes a text feature which has the name specified as the key in ExplanationMetadata.inputs. The baseline of the empty feature is chosen by Vertex AI. For Vertex AI-provided Tensorflow images, the key can be any friendly name of the feature. Once specified, featureAttributions are keyed by this key (if not grouped with another feature). For custom images, the key must match with the key in instance.
outputs This property is required. Map<String>
Map from output names to output metadata. For Vertex AI-provided Tensorflow images, keys can be any user defined string that consists of any UTF-8 characters. For custom images, keys are the name of the output field in the prediction to be explained. Currently only one key is allowed.
featureAttributionsSchemaUri String
Points to a YAML file stored on Google Cloud Storage describing the format of the feature attributions. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML tabular Models always have this field populated by Vertex AI. Note: The URI given on output may be different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
latentSpaceSource String
Name of the source to generate embeddings for example based explanations.

GoogleCloudAiplatformV1ExplanationMetadataResponse
, GoogleCloudAiplatformV1ExplanationMetadataResponseArgs

FeatureAttributionsSchemaUri This property is required. string
Points to a YAML file stored on Google Cloud Storage describing the format of the feature attributions. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML tabular Models always have this field populated by Vertex AI. Note: The URI given on output may be different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
Inputs This property is required. Dictionary<string, string>
Map from feature names to feature input metadata. Keys are the name of the features. Values are the specification of the feature. An empty InputMetadata is valid. It describes a text feature which has the name specified as the key in ExplanationMetadata.inputs. The baseline of the empty feature is chosen by Vertex AI. For Vertex AI-provided Tensorflow images, the key can be any friendly name of the feature. Once specified, featureAttributions are keyed by this key (if not grouped with another feature). For custom images, the key must match with the key in instance.
LatentSpaceSource This property is required. string
Name of the source to generate embeddings for example based explanations.
Outputs This property is required. Dictionary<string, string>
Map from output names to output metadata. For Vertex AI-provided Tensorflow images, keys can be any user defined string that consists of any UTF-8 characters. For custom images, keys are the name of the output field in the prediction to be explained. Currently only one key is allowed.
FeatureAttributionsSchemaUri This property is required. string
Points to a YAML file stored on Google Cloud Storage describing the format of the feature attributions. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML tabular Models always have this field populated by Vertex AI. Note: The URI given on output may be different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
Inputs This property is required. map[string]string
Map from feature names to feature input metadata. Keys are the name of the features. Values are the specification of the feature. An empty InputMetadata is valid. It describes a text feature which has the name specified as the key in ExplanationMetadata.inputs. The baseline of the empty feature is chosen by Vertex AI. For Vertex AI-provided Tensorflow images, the key can be any friendly name of the feature. Once specified, featureAttributions are keyed by this key (if not grouped with another feature). For custom images, the key must match with the key in instance.
LatentSpaceSource This property is required. string
Name of the source to generate embeddings for example based explanations.
Outputs This property is required. map[string]string
Map from output names to output metadata. For Vertex AI-provided Tensorflow images, keys can be any user defined string that consists of any UTF-8 characters. For custom images, keys are the name of the output field in the prediction to be explained. Currently only one key is allowed.
featureAttributionsSchemaUri This property is required. String
Points to a YAML file stored on Google Cloud Storage describing the format of the feature attributions. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML tabular Models always have this field populated by Vertex AI. Note: The URI given on output may be different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
inputs This property is required. Map<String,String>
Map from feature names to feature input metadata. Keys are the name of the features. Values are the specification of the feature. An empty InputMetadata is valid. It describes a text feature which has the name specified as the key in ExplanationMetadata.inputs. The baseline of the empty feature is chosen by Vertex AI. For Vertex AI-provided Tensorflow images, the key can be any friendly name of the feature. Once specified, featureAttributions are keyed by this key (if not grouped with another feature). For custom images, the key must match with the key in instance.
latentSpaceSource This property is required. String
Name of the source to generate embeddings for example based explanations.
outputs This property is required. Map<String,String>
Map from output names to output metadata. For Vertex AI-provided Tensorflow images, keys can be any user defined string that consists of any UTF-8 characters. For custom images, keys are the name of the output field in the prediction to be explained. Currently only one key is allowed.
featureAttributionsSchemaUri This property is required. string
Points to a YAML file stored on Google Cloud Storage describing the format of the feature attributions. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML tabular Models always have this field populated by Vertex AI. Note: The URI given on output may be different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
inputs This property is required. {[key: string]: string}
Map from feature names to feature input metadata. Keys are the name of the features. Values are the specification of the feature. An empty InputMetadata is valid. It describes a text feature which has the name specified as the key in ExplanationMetadata.inputs. The baseline of the empty feature is chosen by Vertex AI. For Vertex AI-provided Tensorflow images, the key can be any friendly name of the feature. Once specified, featureAttributions are keyed by this key (if not grouped with another feature). For custom images, the key must match with the key in instance.
latentSpaceSource This property is required. string
Name of the source to generate embeddings for example based explanations.
outputs This property is required. {[key: string]: string}
Map from output names to output metadata. For Vertex AI-provided Tensorflow images, keys can be any user defined string that consists of any UTF-8 characters. For custom images, keys are the name of the output field in the prediction to be explained. Currently only one key is allowed.
feature_attributions_schema_uri This property is required. str
Points to a YAML file stored on Google Cloud Storage describing the format of the feature attributions. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML tabular Models always have this field populated by Vertex AI. Note: The URI given on output may be different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
inputs This property is required. Mapping[str, str]
Map from feature names to feature input metadata. Keys are the name of the features. Values are the specification of the feature. An empty InputMetadata is valid. It describes a text feature which has the name specified as the key in ExplanationMetadata.inputs. The baseline of the empty feature is chosen by Vertex AI. For Vertex AI-provided Tensorflow images, the key can be any friendly name of the feature. Once specified, featureAttributions are keyed by this key (if not grouped with another feature). For custom images, the key must match with the key in instance.
latent_space_source This property is required. str
Name of the source to generate embeddings for example based explanations.
outputs This property is required. Mapping[str, str]
Map from output names to output metadata. For Vertex AI-provided Tensorflow images, keys can be any user defined string that consists of any UTF-8 characters. For custom images, keys are the name of the output field in the prediction to be explained. Currently only one key is allowed.
featureAttributionsSchemaUri This property is required. String
Points to a YAML file stored on Google Cloud Storage describing the format of the feature attributions. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML tabular Models always have this field populated by Vertex AI. Note: The URI given on output may be different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
inputs This property is required. Map<String>
Map from feature names to feature input metadata. Keys are the name of the features. Values are the specification of the feature. An empty InputMetadata is valid. It describes a text feature which has the name specified as the key in ExplanationMetadata.inputs. The baseline of the empty feature is chosen by Vertex AI. For Vertex AI-provided Tensorflow images, the key can be any friendly name of the feature. Once specified, featureAttributions are keyed by this key (if not grouped with another feature). For custom images, the key must match with the key in instance.
latentSpaceSource This property is required. String
Name of the source to generate embeddings for example based explanations.
outputs This property is required. Map<String>
Map from output names to output metadata. For Vertex AI-provided Tensorflow images, keys can be any user defined string that consists of any UTF-8 characters. For custom images, keys are the name of the output field in the prediction to be explained. Currently only one key is allowed.

GoogleCloudAiplatformV1ExplanationParameters
, GoogleCloudAiplatformV1ExplanationParametersArgs

Examples Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1Examples
Example-based explanations that returns the nearest neighbors from the provided dataset.
IntegratedGradientsAttribution Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1IntegratedGradientsAttribution
An attribution method that computes Aumann-Shapley values taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
OutputIndices List<object>
If populated, only returns attributions that have output_index contained in output_indices. It must be an ndarray of integers, with the same shape of the output it's explaining. If not populated, returns attributions for top_k indices of outputs. If neither top_k nor output_indices is populated, returns the argmax index of the outputs. Only applicable to Models that predict multiple outputs (e,g, multi-class Models that predict multiple classes).
SampledShapleyAttribution Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1SampledShapleyAttribution
An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. Refer to this paper for model details: https://arxiv.org/abs/1306.4265.
TopK int
If populated, returns attributions for top K indices of outputs (defaults to 1). Only applies to Models that predicts more than one outputs (e,g, multi-class Models). When set to -1, returns explanations for all outputs.
XraiAttribution Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1XraiAttribution
An attribution method that redistributes Integrated Gradients attribution to segmented regions, taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 XRAI currently performs better on natural images, like a picture of a house or an animal. If the images are taken in artificial environments, like a lab or manufacturing line, or from diagnostic equipment, like x-rays or quality-control cameras, use Integrated Gradients instead.
Examples GoogleCloudAiplatformV1Examples
Example-based explanations that returns the nearest neighbors from the provided dataset.
IntegratedGradientsAttribution GoogleCloudAiplatformV1IntegratedGradientsAttribution
An attribution method that computes Aumann-Shapley values taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
OutputIndices []interface{}
If populated, only returns attributions that have output_index contained in output_indices. It must be an ndarray of integers, with the same shape of the output it's explaining. If not populated, returns attributions for top_k indices of outputs. If neither top_k nor output_indices is populated, returns the argmax index of the outputs. Only applicable to Models that predict multiple outputs (e,g, multi-class Models that predict multiple classes).
SampledShapleyAttribution GoogleCloudAiplatformV1SampledShapleyAttribution
An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. Refer to this paper for model details: https://arxiv.org/abs/1306.4265.
TopK int
If populated, returns attributions for top K indices of outputs (defaults to 1). Only applies to Models that predicts more than one outputs (e,g, multi-class Models). When set to -1, returns explanations for all outputs.
XraiAttribution GoogleCloudAiplatformV1XraiAttribution
An attribution method that redistributes Integrated Gradients attribution to segmented regions, taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 XRAI currently performs better on natural images, like a picture of a house or an animal. If the images are taken in artificial environments, like a lab or manufacturing line, or from diagnostic equipment, like x-rays or quality-control cameras, use Integrated Gradients instead.
examples GoogleCloudAiplatformV1Examples
Example-based explanations that returns the nearest neighbors from the provided dataset.
integratedGradientsAttribution GoogleCloudAiplatformV1IntegratedGradientsAttribution
An attribution method that computes Aumann-Shapley values taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
outputIndices List<Object>
If populated, only returns attributions that have output_index contained in output_indices. It must be an ndarray of integers, with the same shape of the output it's explaining. If not populated, returns attributions for top_k indices of outputs. If neither top_k nor output_indices is populated, returns the argmax index of the outputs. Only applicable to Models that predict multiple outputs (e,g, multi-class Models that predict multiple classes).
sampledShapleyAttribution GoogleCloudAiplatformV1SampledShapleyAttribution
An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. Refer to this paper for model details: https://arxiv.org/abs/1306.4265.
topK Integer
If populated, returns attributions for top K indices of outputs (defaults to 1). Only applies to Models that predicts more than one outputs (e,g, multi-class Models). When set to -1, returns explanations for all outputs.
xraiAttribution GoogleCloudAiplatformV1XraiAttribution
An attribution method that redistributes Integrated Gradients attribution to segmented regions, taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 XRAI currently performs better on natural images, like a picture of a house or an animal. If the images are taken in artificial environments, like a lab or manufacturing line, or from diagnostic equipment, like x-rays or quality-control cameras, use Integrated Gradients instead.
examples GoogleCloudAiplatformV1Examples
Example-based explanations that returns the nearest neighbors from the provided dataset.
integratedGradientsAttribution GoogleCloudAiplatformV1IntegratedGradientsAttribution
An attribution method that computes Aumann-Shapley values taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
outputIndices any[]
If populated, only returns attributions that have output_index contained in output_indices. It must be an ndarray of integers, with the same shape of the output it's explaining. If not populated, returns attributions for top_k indices of outputs. If neither top_k nor output_indices is populated, returns the argmax index of the outputs. Only applicable to Models that predict multiple outputs (e,g, multi-class Models that predict multiple classes).
sampledShapleyAttribution GoogleCloudAiplatformV1SampledShapleyAttribution
An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. Refer to this paper for model details: https://arxiv.org/abs/1306.4265.
topK number
If populated, returns attributions for top K indices of outputs (defaults to 1). Only applies to Models that predicts more than one outputs (e,g, multi-class Models). When set to -1, returns explanations for all outputs.
xraiAttribution GoogleCloudAiplatformV1XraiAttribution
An attribution method that redistributes Integrated Gradients attribution to segmented regions, taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 XRAI currently performs better on natural images, like a picture of a house or an animal. If the images are taken in artificial environments, like a lab or manufacturing line, or from diagnostic equipment, like x-rays or quality-control cameras, use Integrated Gradients instead.
examples GoogleCloudAiplatformV1Examples
Example-based explanations that returns the nearest neighbors from the provided dataset.
integrated_gradients_attribution GoogleCloudAiplatformV1IntegratedGradientsAttribution
An attribution method that computes Aumann-Shapley values taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
output_indices Sequence[Any]
If populated, only returns attributions that have output_index contained in output_indices. It must be an ndarray of integers, with the same shape of the output it's explaining. If not populated, returns attributions for top_k indices of outputs. If neither top_k nor output_indices is populated, returns the argmax index of the outputs. Only applicable to Models that predict multiple outputs (e,g, multi-class Models that predict multiple classes).
sampled_shapley_attribution GoogleCloudAiplatformV1SampledShapleyAttribution
An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. Refer to this paper for model details: https://arxiv.org/abs/1306.4265.
top_k int
If populated, returns attributions for top K indices of outputs (defaults to 1). Only applies to Models that predicts more than one outputs (e,g, multi-class Models). When set to -1, returns explanations for all outputs.
xrai_attribution GoogleCloudAiplatformV1XraiAttribution
An attribution method that redistributes Integrated Gradients attribution to segmented regions, taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 XRAI currently performs better on natural images, like a picture of a house or an animal. If the images are taken in artificial environments, like a lab or manufacturing line, or from diagnostic equipment, like x-rays or quality-control cameras, use Integrated Gradients instead.
examples Property Map
Example-based explanations that returns the nearest neighbors from the provided dataset.
integratedGradientsAttribution Property Map
An attribution method that computes Aumann-Shapley values taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
outputIndices List<Any>
If populated, only returns attributions that have output_index contained in output_indices. It must be an ndarray of integers, with the same shape of the output it's explaining. If not populated, returns attributions for top_k indices of outputs. If neither top_k nor output_indices is populated, returns the argmax index of the outputs. Only applicable to Models that predict multiple outputs (e,g, multi-class Models that predict multiple classes).
sampledShapleyAttribution Property Map
An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. Refer to this paper for model details: https://arxiv.org/abs/1306.4265.
topK Number
If populated, returns attributions for top K indices of outputs (defaults to 1). Only applies to Models that predicts more than one outputs (e,g, multi-class Models). When set to -1, returns explanations for all outputs.
xraiAttribution Property Map
An attribution method that redistributes Integrated Gradients attribution to segmented regions, taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 XRAI currently performs better on natural images, like a picture of a house or an animal. If the images are taken in artificial environments, like a lab or manufacturing line, or from diagnostic equipment, like x-rays or quality-control cameras, use Integrated Gradients instead.

GoogleCloudAiplatformV1ExplanationParametersResponse
, GoogleCloudAiplatformV1ExplanationParametersResponseArgs

Examples This property is required. Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ExamplesResponse
Example-based explanations that returns the nearest neighbors from the provided dataset.
IntegratedGradientsAttribution This property is required. Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1IntegratedGradientsAttributionResponse
An attribution method that computes Aumann-Shapley values taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
OutputIndices This property is required. List<object>
If populated, only returns attributions that have output_index contained in output_indices. It must be an ndarray of integers, with the same shape of the output it's explaining. If not populated, returns attributions for top_k indices of outputs. If neither top_k nor output_indices is populated, returns the argmax index of the outputs. Only applicable to Models that predict multiple outputs (e,g, multi-class Models that predict multiple classes).
SampledShapleyAttribution This property is required. Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1SampledShapleyAttributionResponse
An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. Refer to this paper for model details: https://arxiv.org/abs/1306.4265.
TopK This property is required. int
If populated, returns attributions for top K indices of outputs (defaults to 1). Only applies to Models that predicts more than one outputs (e,g, multi-class Models). When set to -1, returns explanations for all outputs.
XraiAttribution This property is required. Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1XraiAttributionResponse
An attribution method that redistributes Integrated Gradients attribution to segmented regions, taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 XRAI currently performs better on natural images, like a picture of a house or an animal. If the images are taken in artificial environments, like a lab or manufacturing line, or from diagnostic equipment, like x-rays or quality-control cameras, use Integrated Gradients instead.
Examples This property is required. GoogleCloudAiplatformV1ExamplesResponse
Example-based explanations that returns the nearest neighbors from the provided dataset.
IntegratedGradientsAttribution This property is required. GoogleCloudAiplatformV1IntegratedGradientsAttributionResponse
An attribution method that computes Aumann-Shapley values taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
OutputIndices This property is required. []interface{}
If populated, only returns attributions that have output_index contained in output_indices. It must be an ndarray of integers, with the same shape of the output it's explaining. If not populated, returns attributions for top_k indices of outputs. If neither top_k nor output_indices is populated, returns the argmax index of the outputs. Only applicable to Models that predict multiple outputs (e,g, multi-class Models that predict multiple classes).
SampledShapleyAttribution This property is required. GoogleCloudAiplatformV1SampledShapleyAttributionResponse
An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. Refer to this paper for model details: https://arxiv.org/abs/1306.4265.
TopK This property is required. int
If populated, returns attributions for top K indices of outputs (defaults to 1). Only applies to Models that predicts more than one outputs (e,g, multi-class Models). When set to -1, returns explanations for all outputs.
XraiAttribution This property is required. GoogleCloudAiplatformV1XraiAttributionResponse
An attribution method that redistributes Integrated Gradients attribution to segmented regions, taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 XRAI currently performs better on natural images, like a picture of a house or an animal. If the images are taken in artificial environments, like a lab or manufacturing line, or from diagnostic equipment, like x-rays or quality-control cameras, use Integrated Gradients instead.
examples This property is required. GoogleCloudAiplatformV1ExamplesResponse
Example-based explanations that returns the nearest neighbors from the provided dataset.
integratedGradientsAttribution This property is required. GoogleCloudAiplatformV1IntegratedGradientsAttributionResponse
An attribution method that computes Aumann-Shapley values taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
outputIndices This property is required. List<Object>
If populated, only returns attributions that have output_index contained in output_indices. It must be an ndarray of integers, with the same shape of the output it's explaining. If not populated, returns attributions for top_k indices of outputs. If neither top_k nor output_indices is populated, returns the argmax index of the outputs. Only applicable to Models that predict multiple outputs (e,g, multi-class Models that predict multiple classes).
sampledShapleyAttribution This property is required. GoogleCloudAiplatformV1SampledShapleyAttributionResponse
An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. Refer to this paper for model details: https://arxiv.org/abs/1306.4265.
topK This property is required. Integer
If populated, returns attributions for top K indices of outputs (defaults to 1). Only applies to Models that predicts more than one outputs (e,g, multi-class Models). When set to -1, returns explanations for all outputs.
xraiAttribution This property is required. GoogleCloudAiplatformV1XraiAttributionResponse
An attribution method that redistributes Integrated Gradients attribution to segmented regions, taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 XRAI currently performs better on natural images, like a picture of a house or an animal. If the images are taken in artificial environments, like a lab or manufacturing line, or from diagnostic equipment, like x-rays or quality-control cameras, use Integrated Gradients instead.
examples This property is required. GoogleCloudAiplatformV1ExamplesResponse
Example-based explanations that returns the nearest neighbors from the provided dataset.
integratedGradientsAttribution This property is required. GoogleCloudAiplatformV1IntegratedGradientsAttributionResponse
An attribution method that computes Aumann-Shapley values taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
outputIndices This property is required. any[]
If populated, only returns attributions that have output_index contained in output_indices. It must be an ndarray of integers, with the same shape of the output it's explaining. If not populated, returns attributions for top_k indices of outputs. If neither top_k nor output_indices is populated, returns the argmax index of the outputs. Only applicable to Models that predict multiple outputs (e,g, multi-class Models that predict multiple classes).
sampledShapleyAttribution This property is required. GoogleCloudAiplatformV1SampledShapleyAttributionResponse
An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. Refer to this paper for model details: https://arxiv.org/abs/1306.4265.
topK This property is required. number
If populated, returns attributions for top K indices of outputs (defaults to 1). Only applies to Models that predicts more than one outputs (e,g, multi-class Models). When set to -1, returns explanations for all outputs.
xraiAttribution This property is required. GoogleCloudAiplatformV1XraiAttributionResponse
An attribution method that redistributes Integrated Gradients attribution to segmented regions, taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 XRAI currently performs better on natural images, like a picture of a house or an animal. If the images are taken in artificial environments, like a lab or manufacturing line, or from diagnostic equipment, like x-rays or quality-control cameras, use Integrated Gradients instead.
examples This property is required. GoogleCloudAiplatformV1ExamplesResponse
Example-based explanations that returns the nearest neighbors from the provided dataset.
integrated_gradients_attribution This property is required. GoogleCloudAiplatformV1IntegratedGradientsAttributionResponse
An attribution method that computes Aumann-Shapley values taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
output_indices This property is required. Sequence[Any]
If populated, only returns attributions that have output_index contained in output_indices. It must be an ndarray of integers, with the same shape of the output it's explaining. If not populated, returns attributions for top_k indices of outputs. If neither top_k nor output_indices is populated, returns the argmax index of the outputs. Only applicable to Models that predict multiple outputs (e,g, multi-class Models that predict multiple classes).
sampled_shapley_attribution This property is required. GoogleCloudAiplatformV1SampledShapleyAttributionResponse
An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. Refer to this paper for model details: https://arxiv.org/abs/1306.4265.
top_k This property is required. int
If populated, returns attributions for top K indices of outputs (defaults to 1). Only applies to Models that predicts more than one outputs (e,g, multi-class Models). When set to -1, returns explanations for all outputs.
xrai_attribution This property is required. GoogleCloudAiplatformV1XraiAttributionResponse
An attribution method that redistributes Integrated Gradients attribution to segmented regions, taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 XRAI currently performs better on natural images, like a picture of a house or an animal. If the images are taken in artificial environments, like a lab or manufacturing line, or from diagnostic equipment, like x-rays or quality-control cameras, use Integrated Gradients instead.
examples This property is required. Property Map
Example-based explanations that returns the nearest neighbors from the provided dataset.
integratedGradientsAttribution This property is required. Property Map
An attribution method that computes Aumann-Shapley values taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
outputIndices This property is required. List<Any>
If populated, only returns attributions that have output_index contained in output_indices. It must be an ndarray of integers, with the same shape of the output it's explaining. If not populated, returns attributions for top_k indices of outputs. If neither top_k nor output_indices is populated, returns the argmax index of the outputs. Only applicable to Models that predict multiple outputs (e,g, multi-class Models that predict multiple classes).
sampledShapleyAttribution This property is required. Property Map
An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. Refer to this paper for model details: https://arxiv.org/abs/1306.4265.
topK This property is required. Number
If populated, returns attributions for top K indices of outputs (defaults to 1). Only applies to Models that predicts more than one outputs (e,g, multi-class Models). When set to -1, returns explanations for all outputs.
xraiAttribution This property is required. Property Map
An attribution method that redistributes Integrated Gradients attribution to segmented regions, taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 XRAI currently performs better on natural images, like a picture of a house or an animal. If the images are taken in artificial environments, like a lab or manufacturing line, or from diagnostic equipment, like x-rays or quality-control cameras, use Integrated Gradients instead.

GoogleCloudAiplatformV1ExplanationSpec
, GoogleCloudAiplatformV1ExplanationSpecArgs

Parameters This property is required. Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ExplanationParameters
Parameters that configure explaining of the Model's predictions.
Metadata Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ExplanationMetadata
Optional. Metadata describing the Model's input and output for explanation.
Parameters This property is required. GoogleCloudAiplatformV1ExplanationParameters
Parameters that configure explaining of the Model's predictions.
Metadata GoogleCloudAiplatformV1ExplanationMetadata
Optional. Metadata describing the Model's input and output for explanation.
parameters This property is required. GoogleCloudAiplatformV1ExplanationParameters
Parameters that configure explaining of the Model's predictions.
metadata GoogleCloudAiplatformV1ExplanationMetadata
Optional. Metadata describing the Model's input and output for explanation.
parameters This property is required. GoogleCloudAiplatformV1ExplanationParameters
Parameters that configure explaining of the Model's predictions.
metadata GoogleCloudAiplatformV1ExplanationMetadata
Optional. Metadata describing the Model's input and output for explanation.
parameters This property is required. GoogleCloudAiplatformV1ExplanationParameters
Parameters that configure explaining of the Model's predictions.
metadata GoogleCloudAiplatformV1ExplanationMetadata
Optional. Metadata describing the Model's input and output for explanation.
parameters This property is required. Property Map
Parameters that configure explaining of the Model's predictions.
metadata Property Map
Optional. Metadata describing the Model's input and output for explanation.

GoogleCloudAiplatformV1ExplanationSpecResponse
, GoogleCloudAiplatformV1ExplanationSpecResponseArgs

Metadata This property is required. Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ExplanationMetadataResponse
Optional. Metadata describing the Model's input and output for explanation.
Parameters This property is required. Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ExplanationParametersResponse
Parameters that configure explaining of the Model's predictions.
Metadata This property is required. GoogleCloudAiplatformV1ExplanationMetadataResponse
Optional. Metadata describing the Model's input and output for explanation.
Parameters This property is required. GoogleCloudAiplatformV1ExplanationParametersResponse
Parameters that configure explaining of the Model's predictions.
metadata This property is required. GoogleCloudAiplatformV1ExplanationMetadataResponse
Optional. Metadata describing the Model's input and output for explanation.
parameters This property is required. GoogleCloudAiplatformV1ExplanationParametersResponse
Parameters that configure explaining of the Model's predictions.
metadata This property is required. GoogleCloudAiplatformV1ExplanationMetadataResponse
Optional. Metadata describing the Model's input and output for explanation.
parameters This property is required. GoogleCloudAiplatformV1ExplanationParametersResponse
Parameters that configure explaining of the Model's predictions.
metadata This property is required. GoogleCloudAiplatformV1ExplanationMetadataResponse
Optional. Metadata describing the Model's input and output for explanation.
parameters This property is required. GoogleCloudAiplatformV1ExplanationParametersResponse
Parameters that configure explaining of the Model's predictions.
metadata This property is required. Property Map
Optional. Metadata describing the Model's input and output for explanation.
parameters This property is required. Property Map
Parameters that configure explaining of the Model's predictions.

GoogleCloudAiplatformV1FeatureNoiseSigma
, GoogleCloudAiplatformV1FeatureNoiseSigmaArgs

NoiseSigma []GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeature
Noise sigma per feature. No noise is added to features that are not set.
noiseSigma List<GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeature>
Noise sigma per feature. No noise is added to features that are not set.
noiseSigma GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeature[]
Noise sigma per feature. No noise is added to features that are not set.
noise_sigma Sequence[GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeature]
Noise sigma per feature. No noise is added to features that are not set.
noiseSigma List<Property Map>
Noise sigma per feature. No noise is added to features that are not set.

GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeature
, GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeatureArgs

Name string
The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
Sigma double
This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.
Name string
The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
Sigma float64
This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.
name String
The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
sigma Double
This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.
name string
The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
sigma number
This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.
name str
The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
sigma float
This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.
name String
The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
sigma Number
This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.

GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeatureResponse
, GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeatureResponseArgs

Name This property is required. string
The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
Sigma This property is required. double
This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.
Name This property is required. string
The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
Sigma This property is required. float64
This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.
name This property is required. String
The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
sigma This property is required. Double
This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.
name This property is required. string
The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
sigma This property is required. number
This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.
name This property is required. str
The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
sigma This property is required. float
This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.
name This property is required. String
The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
sigma This property is required. Number
This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.

GoogleCloudAiplatformV1FeatureNoiseSigmaResponse
, GoogleCloudAiplatformV1FeatureNoiseSigmaResponseArgs

NoiseSigma This property is required. List<Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeatureResponse>
Noise sigma per feature. No noise is added to features that are not set.
NoiseSigma This property is required. []GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeatureResponse
Noise sigma per feature. No noise is added to features that are not set.
noiseSigma This property is required. List<GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeatureResponse>
Noise sigma per feature. No noise is added to features that are not set.
noiseSigma This property is required. GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeatureResponse[]
Noise sigma per feature. No noise is added to features that are not set.
noise_sigma This property is required. Sequence[GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeatureResponse]
Noise sigma per feature. No noise is added to features that are not set.
noiseSigma This property is required. List<Property Map>
Noise sigma per feature. No noise is added to features that are not set.

GoogleCloudAiplatformV1GcsDestination
, GoogleCloudAiplatformV1GcsDestinationArgs

OutputUriPrefix This property is required. string
Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.
OutputUriPrefix This property is required. string
Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.
outputUriPrefix This property is required. String
Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.
outputUriPrefix This property is required. string
Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.
output_uri_prefix This property is required. str
Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.
outputUriPrefix This property is required. String
Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.

GoogleCloudAiplatformV1GcsDestinationResponse
, GoogleCloudAiplatformV1GcsDestinationResponseArgs

OutputUriPrefix This property is required. string
Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.
OutputUriPrefix This property is required. string
Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.
outputUriPrefix This property is required. String
Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.
outputUriPrefix This property is required. string
Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.
output_uri_prefix This property is required. str
Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.
outputUriPrefix This property is required. String
Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.

GoogleCloudAiplatformV1GcsSource
, GoogleCloudAiplatformV1GcsSourceArgs

Uris This property is required. List<string>
Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
Uris This property is required. []string
Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
uris This property is required. List<String>
Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
uris This property is required. string[]
Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
uris This property is required. Sequence[str]
Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
uris This property is required. List<String>
Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.

GoogleCloudAiplatformV1GcsSourceResponse
, GoogleCloudAiplatformV1GcsSourceResponseArgs

Uris This property is required. List<string>
Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
Uris This property is required. []string
Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
uris This property is required. List<String>
Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
uris This property is required. string[]
Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
uris This property is required. Sequence[str]
Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
uris This property is required. List<String>
Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.

GoogleCloudAiplatformV1IntegratedGradientsAttribution
, GoogleCloudAiplatformV1IntegratedGradientsAttributionArgs

StepCount This property is required. int
The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.
BlurBaselineConfig Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1BlurBaselineConfig
Config for IG with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
SmoothGradConfig Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1SmoothGradConfig
Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
StepCount This property is required. int
The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.
BlurBaselineConfig GoogleCloudAiplatformV1BlurBaselineConfig
Config for IG with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
SmoothGradConfig GoogleCloudAiplatformV1SmoothGradConfig
Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
stepCount This property is required. Integer
The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.
blurBaselineConfig GoogleCloudAiplatformV1BlurBaselineConfig
Config for IG with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
smoothGradConfig GoogleCloudAiplatformV1SmoothGradConfig
Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
stepCount This property is required. number
The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.
blurBaselineConfig GoogleCloudAiplatformV1BlurBaselineConfig
Config for IG with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
smoothGradConfig GoogleCloudAiplatformV1SmoothGradConfig
Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
step_count This property is required. int
The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.
blur_baseline_config GoogleCloudAiplatformV1BlurBaselineConfig
Config for IG with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
smooth_grad_config GoogleCloudAiplatformV1SmoothGradConfig
Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
stepCount This property is required. Number
The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.
blurBaselineConfig Property Map
Config for IG with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
smoothGradConfig Property Map
Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf

GoogleCloudAiplatformV1IntegratedGradientsAttributionResponse
, GoogleCloudAiplatformV1IntegratedGradientsAttributionResponseArgs

BlurBaselineConfig This property is required. Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1BlurBaselineConfigResponse
Config for IG with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
SmoothGradConfig This property is required. Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1SmoothGradConfigResponse
Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
StepCount This property is required. int
The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.
BlurBaselineConfig This property is required. GoogleCloudAiplatformV1BlurBaselineConfigResponse
Config for IG with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
SmoothGradConfig This property is required. GoogleCloudAiplatformV1SmoothGradConfigResponse
Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
StepCount This property is required. int
The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.
blurBaselineConfig This property is required. GoogleCloudAiplatformV1BlurBaselineConfigResponse
Config for IG with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
smoothGradConfig This property is required. GoogleCloudAiplatformV1SmoothGradConfigResponse
Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
stepCount This property is required. Integer
The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.
blurBaselineConfig This property is required. GoogleCloudAiplatformV1BlurBaselineConfigResponse
Config for IG with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
smoothGradConfig This property is required. GoogleCloudAiplatformV1SmoothGradConfigResponse
Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
stepCount This property is required. number
The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.
blur_baseline_config This property is required. GoogleCloudAiplatformV1BlurBaselineConfigResponse
Config for IG with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
smooth_grad_config This property is required. GoogleCloudAiplatformV1SmoothGradConfigResponse
Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
step_count This property is required. int
The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.
blurBaselineConfig This property is required. Property Map
Config for IG with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
smoothGradConfig This property is required. Property Map
Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
stepCount This property is required. Number
The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.

GoogleCloudAiplatformV1MachineSpec
, GoogleCloudAiplatformV1MachineSpecArgs

AcceleratorCount int
The number of accelerators to attach to the machine.
AcceleratorType Pulumi.GoogleNative.Aiplatform.V1.GoogleCloudAiplatformV1MachineSpecAcceleratorType
Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
MachineType string
Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
TpuTopology string
Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
AcceleratorCount int
The number of accelerators to attach to the machine.
AcceleratorType GoogleCloudAiplatformV1MachineSpecAcceleratorType
Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
MachineType string
Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
TpuTopology string
Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
acceleratorCount Integer
The number of accelerators to attach to the machine.
acceleratorType GoogleCloudAiplatformV1MachineSpecAcceleratorType
Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
machineType String
Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
tpuTopology String
Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
acceleratorCount number
The number of accelerators to attach to the machine.
acceleratorType GoogleCloudAiplatformV1MachineSpecAcceleratorType
Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
machineType string
Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
tpuTopology string
Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
accelerator_count int
The number of accelerators to attach to the machine.
accelerator_type GoogleCloudAiplatformV1MachineSpecAcceleratorType
Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
machine_type str
Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
tpu_topology str
Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
acceleratorCount Number
The number of accelerators to attach to the machine.
acceleratorType "ACCELERATOR_TYPE_UNSPECIFIED" | "NVIDIA_TESLA_K80" | "NVIDIA_TESLA_P100" | "NVIDIA_TESLA_V100" | "NVIDIA_TESLA_P4" | "NVIDIA_TESLA_T4" | "NVIDIA_TESLA_A100" | "NVIDIA_A100_80GB" | "NVIDIA_L4" | "TPU_V2" | "TPU_V3" | "TPU_V4_POD"
Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
machineType String
Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
tpuTopology String
Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").

GoogleCloudAiplatformV1MachineSpecAcceleratorType
, GoogleCloudAiplatformV1MachineSpecAcceleratorTypeArgs

AcceleratorTypeUnspecified
ACCELERATOR_TYPE_UNSPECIFIEDUnspecified accelerator type, which means no accelerator.
NvidiaTeslaK80
NVIDIA_TESLA_K80Nvidia Tesla K80 GPU.
NvidiaTeslaP100
NVIDIA_TESLA_P100Nvidia Tesla P100 GPU.
NvidiaTeslaV100
NVIDIA_TESLA_V100Nvidia Tesla V100 GPU.
NvidiaTeslaP4
NVIDIA_TESLA_P4Nvidia Tesla P4 GPU.
NvidiaTeslaT4
NVIDIA_TESLA_T4Nvidia Tesla T4 GPU.
NvidiaTeslaA100
NVIDIA_TESLA_A100Nvidia Tesla A100 GPU.
NvidiaA10080gb
NVIDIA_A100_80GBNvidia A100 80GB GPU.
NvidiaL4
NVIDIA_L4Nvidia L4 GPU.
TpuV2
TPU_V2TPU v2.
TpuV3
TPU_V3TPU v3.
TpuV4Pod
TPU_V4_PODTPU v4.
GoogleCloudAiplatformV1MachineSpecAcceleratorTypeAcceleratorTypeUnspecified
ACCELERATOR_TYPE_UNSPECIFIEDUnspecified accelerator type, which means no accelerator.
GoogleCloudAiplatformV1MachineSpecAcceleratorTypeNvidiaTeslaK80
NVIDIA_TESLA_K80Nvidia Tesla K80 GPU.
GoogleCloudAiplatformV1MachineSpecAcceleratorTypeNvidiaTeslaP100
NVIDIA_TESLA_P100Nvidia Tesla P100 GPU.
GoogleCloudAiplatformV1MachineSpecAcceleratorTypeNvidiaTeslaV100
NVIDIA_TESLA_V100Nvidia Tesla V100 GPU.
GoogleCloudAiplatformV1MachineSpecAcceleratorTypeNvidiaTeslaP4
NVIDIA_TESLA_P4Nvidia Tesla P4 GPU.
GoogleCloudAiplatformV1MachineSpecAcceleratorTypeNvidiaTeslaT4
NVIDIA_TESLA_T4Nvidia Tesla T4 GPU.
GoogleCloudAiplatformV1MachineSpecAcceleratorTypeNvidiaTeslaA100
NVIDIA_TESLA_A100Nvidia Tesla A100 GPU.
GoogleCloudAiplatformV1MachineSpecAcceleratorTypeNvidiaA10080gb
NVIDIA_A100_80GBNvidia A100 80GB GPU.
GoogleCloudAiplatformV1MachineSpecAcceleratorTypeNvidiaL4
NVIDIA_L4Nvidia L4 GPU.
GoogleCloudAiplatformV1MachineSpecAcceleratorTypeTpuV2
TPU_V2TPU v2.
GoogleCloudAiplatformV1MachineSpecAcceleratorTypeTpuV3
TPU_V3TPU v3.
GoogleCloudAiplatformV1MachineSpecAcceleratorTypeTpuV4Pod
TPU_V4_PODTPU v4.
AcceleratorTypeUnspecified
ACCELERATOR_TYPE_UNSPECIFIEDUnspecified accelerator type, which means no accelerator.
NvidiaTeslaK80
NVIDIA_TESLA_K80Nvidia Tesla K80 GPU.
NvidiaTeslaP100
NVIDIA_TESLA_P100Nvidia Tesla P100 GPU.
NvidiaTeslaV100
NVIDIA_TESLA_V100Nvidia Tesla V100 GPU.
NvidiaTeslaP4
NVIDIA_TESLA_P4Nvidia Tesla P4 GPU.
NvidiaTeslaT4
NVIDIA_TESLA_T4Nvidia Tesla T4 GPU.
NvidiaTeslaA100
NVIDIA_TESLA_A100Nvidia Tesla A100 GPU.
NvidiaA10080gb
NVIDIA_A100_80GBNvidia A100 80GB GPU.
NvidiaL4
NVIDIA_L4Nvidia L4 GPU.
TpuV2
TPU_V2TPU v2.
TpuV3
TPU_V3TPU v3.
TpuV4Pod
TPU_V4_PODTPU v4.
AcceleratorTypeUnspecified
ACCELERATOR_TYPE_UNSPECIFIEDUnspecified accelerator type, which means no accelerator.
NvidiaTeslaK80
NVIDIA_TESLA_K80Nvidia Tesla K80 GPU.
NvidiaTeslaP100
NVIDIA_TESLA_P100Nvidia Tesla P100 GPU.
NvidiaTeslaV100
NVIDIA_TESLA_V100Nvidia Tesla V100 GPU.
NvidiaTeslaP4
NVIDIA_TESLA_P4Nvidia Tesla P4 GPU.
NvidiaTeslaT4
NVIDIA_TESLA_T4Nvidia Tesla T4 GPU.
NvidiaTeslaA100
NVIDIA_TESLA_A100Nvidia Tesla A100 GPU.
NvidiaA10080gb
NVIDIA_A100_80GBNvidia A100 80GB GPU.
NvidiaL4
NVIDIA_L4Nvidia L4 GPU.
TpuV2
TPU_V2TPU v2.
TpuV3
TPU_V3TPU v3.
TpuV4Pod
TPU_V4_PODTPU v4.
ACCELERATOR_TYPE_UNSPECIFIED
ACCELERATOR_TYPE_UNSPECIFIEDUnspecified accelerator type, which means no accelerator.
NVIDIA_TESLA_K80
NVIDIA_TESLA_K80Nvidia Tesla K80 GPU.
NVIDIA_TESLA_P100
NVIDIA_TESLA_P100Nvidia Tesla P100 GPU.
NVIDIA_TESLA_V100
NVIDIA_TESLA_V100Nvidia Tesla V100 GPU.
NVIDIA_TESLA_P4
NVIDIA_TESLA_P4Nvidia Tesla P4 GPU.
NVIDIA_TESLA_T4
NVIDIA_TESLA_T4Nvidia Tesla T4 GPU.
NVIDIA_TESLA_A100
NVIDIA_TESLA_A100Nvidia Tesla A100 GPU.
NVIDIA_A10080GB
NVIDIA_A100_80GBNvidia A100 80GB GPU.
NVIDIA_L4
NVIDIA_L4Nvidia L4 GPU.
TPU_V2
TPU_V2TPU v2.
TPU_V3
TPU_V3TPU v3.
TPU_V4_POD
TPU_V4_PODTPU v4.
"ACCELERATOR_TYPE_UNSPECIFIED"
ACCELERATOR_TYPE_UNSPECIFIEDUnspecified accelerator type, which means no accelerator.
"NVIDIA_TESLA_K80"
NVIDIA_TESLA_K80Nvidia Tesla K80 GPU.
"NVIDIA_TESLA_P100"
NVIDIA_TESLA_P100Nvidia Tesla P100 GPU.
"NVIDIA_TESLA_V100"
NVIDIA_TESLA_V100Nvidia Tesla V100 GPU.
"NVIDIA_TESLA_P4"
NVIDIA_TESLA_P4Nvidia Tesla P4 GPU.
"NVIDIA_TESLA_T4"
NVIDIA_TESLA_T4Nvidia Tesla T4 GPU.
"NVIDIA_TESLA_A100"
NVIDIA_TESLA_A100Nvidia Tesla A100 GPU.
"NVIDIA_A100_80GB"
NVIDIA_A100_80GBNvidia A100 80GB GPU.
"NVIDIA_L4"
NVIDIA_L4Nvidia L4 GPU.
"TPU_V2"
TPU_V2TPU v2.
"TPU_V3"
TPU_V3TPU v3.
"TPU_V4_POD"
TPU_V4_PODTPU v4.

GoogleCloudAiplatformV1MachineSpecResponse
, GoogleCloudAiplatformV1MachineSpecResponseArgs

AcceleratorCount This property is required. int
The number of accelerators to attach to the machine.
AcceleratorType This property is required. string
Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
MachineType This property is required. string
Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
TpuTopology This property is required. string
Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
AcceleratorCount This property is required. int
The number of accelerators to attach to the machine.
AcceleratorType This property is required. string
Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
MachineType This property is required. string
Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
TpuTopology This property is required. string
Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
acceleratorCount This property is required. Integer
The number of accelerators to attach to the machine.
acceleratorType This property is required. String
Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
machineType This property is required. String
Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
tpuTopology This property is required. String
Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
acceleratorCount This property is required. number
The number of accelerators to attach to the machine.
acceleratorType This property is required. string
Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
machineType This property is required. string
Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
tpuTopology This property is required. string
Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
accelerator_count This property is required. int
The number of accelerators to attach to the machine.
accelerator_type This property is required. str
Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
machine_type This property is required. str
Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
tpu_topology This property is required. str
Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
acceleratorCount This property is required. Number
The number of accelerators to attach to the machine.
acceleratorType This property is required. String
Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
machineType This property is required. String
Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
tpuTopology This property is required. String
Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").

GoogleCloudAiplatformV1ManualBatchTuningParameters
, GoogleCloudAiplatformV1ManualBatchTuningParametersArgs

BatchSize int
Immutable. The number of the records (e.g. instances) of the operation given in each batch to a machine replica. Machine type, and size of a single record should be considered when setting this parameter, higher value speeds up the batch operation's execution, but too high value will result in a whole batch not fitting in a machine's memory, and the whole operation will fail. The default value is 64.
BatchSize int
Immutable. The number of the records (e.g. instances) of the operation given in each batch to a machine replica. Machine type, and size of a single record should be considered when setting this parameter, higher value speeds up the batch operation's execution, but too high value will result in a whole batch not fitting in a machine's memory, and the whole operation will fail. The default value is 64.
batchSize Integer
Immutable. The number of the records (e.g. instances) of the operation given in each batch to a machine replica. Machine type, and size of a single record should be considered when setting this parameter, higher value speeds up the batch operation's execution, but too high value will result in a whole batch not fitting in a machine's memory, and the whole operation will fail. The default value is 64.
batchSize number
Immutable. The number of the records (e.g. instances) of the operation given in each batch to a machine replica. Machine type, and size of a single record should be considered when setting this parameter, higher value speeds up the batch operation's execution, but too high value will result in a whole batch not fitting in a machine's memory, and the whole operation will fail. The default value is 64.
batch_size int
Immutable. The number of the records (e.g. instances) of the operation given in each batch to a machine replica. Machine type, and size of a single record should be considered when setting this parameter, higher value speeds up the batch operation's execution, but too high value will result in a whole batch not fitting in a machine's memory, and the whole operation will fail. The default value is 64.
batchSize Number
Immutable. The number of the records (e.g. instances) of the operation given in each batch to a machine replica. Machine type, and size of a single record should be considered when setting this parameter, higher value speeds up the batch operation's execution, but too high value will result in a whole batch not fitting in a machine's memory, and the whole operation will fail. The default value is 64.

GoogleCloudAiplatformV1ManualBatchTuningParametersResponse
, GoogleCloudAiplatformV1ManualBatchTuningParametersResponseArgs

BatchSize This property is required. int
Immutable. The number of the records (e.g. instances) of the operation given in each batch to a machine replica. Machine type, and size of a single record should be considered when setting this parameter, higher value speeds up the batch operation's execution, but too high value will result in a whole batch not fitting in a machine's memory, and the whole operation will fail. The default value is 64.
BatchSize This property is required. int
Immutable. The number of the records (e.g. instances) of the operation given in each batch to a machine replica. Machine type, and size of a single record should be considered when setting this parameter, higher value speeds up the batch operation's execution, but too high value will result in a whole batch not fitting in a machine's memory, and the whole operation will fail. The default value is 64.
batchSize This property is required. Integer
Immutable. The number of the records (e.g. instances) of the operation given in each batch to a machine replica. Machine type, and size of a single record should be considered when setting this parameter, higher value speeds up the batch operation's execution, but too high value will result in a whole batch not fitting in a machine's memory, and the whole operation will fail. The default value is 64.
batchSize This property is required. number
Immutable. The number of the records (e.g. instances) of the operation given in each batch to a machine replica. Machine type, and size of a single record should be considered when setting this parameter, higher value speeds up the batch operation's execution, but too high value will result in a whole batch not fitting in a machine's memory, and the whole operation will fail. The default value is 64.
batch_size This property is required. int
Immutable. The number of the records (e.g. instances) of the operation given in each batch to a machine replica. Machine type, and size of a single record should be considered when setting this parameter, higher value speeds up the batch operation's execution, but too high value will result in a whole batch not fitting in a machine's memory, and the whole operation will fail. The default value is 64.
batchSize This property is required. Number
Immutable. The number of the records (e.g. instances) of the operation given in each batch to a machine replica. Machine type, and size of a single record should be considered when setting this parameter, higher value speeds up the batch operation's execution, but too high value will result in a whole batch not fitting in a machine's memory, and the whole operation will fail. The default value is 64.

GoogleCloudAiplatformV1ModelContainerSpec
, GoogleCloudAiplatformV1ModelContainerSpecArgs

ImageUri This property is required. string
Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
Args List<string>
Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD. Specify this field as an array of executable and arguments, similar to a Docker CMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the args field of the Kubernetes Containers v1 core API.
Command List<string>
Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT. Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT's "exec" form, not its "shell" form. If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD, if either exists. If this field is not specified and the container does not have an ENTRYPOINT, then refer to the Docker documentation about how CMD and ENTRYPOINT interact. If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the command field of the Kubernetes Containers v1 core API.
DeploymentTimeout string
Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
Env List<Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1EnvVar>
Immutable. List of environment variables to set in the container. After the container starts running, code running in the container can read these environment variables. Additionally, the command and args fields can reference these variables. Later entries in this list can also reference earlier entries. For example, the following example sets the variable VAR_2 to have the value foo bar: json [ { "name": "VAR_1", "value": "foo" }, { "name": "VAR_2", "value": "$(VAR_1) bar" } ] If you switch the order of the variables in the example, then the expansion does not occur. This field corresponds to the env field of the Kubernetes Containers v1 core API.
HealthProbe Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1Probe
Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
HealthRoute string
Immutable. HTTP path on the container to send health checks to. Vertex AI intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about health checks. For example, if you set this field to /bar, then Vertex AI intermittently sends a GET request to the /bar path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/ DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
Ports List<Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1Port>
Immutable. List of ports to expose from the container. Vertex AI sends any prediction requests that it receives to the first port on this list. Vertex AI also sends liveness and health checks to this port. If you do not specify this field, it defaults to following value: json [ { "containerPort": 8080 } ] Vertex AI does not use ports other than the first one listed. This field corresponds to the ports field of the Kubernetes Containers v1 core API.
PredictRoute string
Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using projects.locations.endpoints.predict to this path on the container's IP address and port. Vertex AI then returns the container's response in the API response. For example, if you set this field to /foo, then when Vertex AI receives a prediction request, it forwards the request body in a POST request to the /foo path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
SharedMemorySizeMb string
Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes. TODO (b/306244185): Revise documentation before exposing.
StartupProbe Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1Probe
Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.
ImageUri This property is required. string
Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
Args []string
Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD. Specify this field as an array of executable and arguments, similar to a Docker CMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the args field of the Kubernetes Containers v1 core API.
Command []string
Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT. Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT's "exec" form, not its "shell" form. If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD, if either exists. If this field is not specified and the container does not have an ENTRYPOINT, then refer to the Docker documentation about how CMD and ENTRYPOINT interact. If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the command field of the Kubernetes Containers v1 core API.
DeploymentTimeout string
Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
Env []GoogleCloudAiplatformV1EnvVar
Immutable. List of environment variables to set in the container. After the container starts running, code running in the container can read these environment variables. Additionally, the command and args fields can reference these variables. Later entries in this list can also reference earlier entries. For example, the following example sets the variable VAR_2 to have the value foo bar: json [ { "name": "VAR_1", "value": "foo" }, { "name": "VAR_2", "value": "$(VAR_1) bar" } ] If you switch the order of the variables in the example, then the expansion does not occur. This field corresponds to the env field of the Kubernetes Containers v1 core API.
HealthProbe GoogleCloudAiplatformV1Probe
Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
HealthRoute string
Immutable. HTTP path on the container to send health checks to. Vertex AI intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about health checks. For example, if you set this field to /bar, then Vertex AI intermittently sends a GET request to the /bar path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/ DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
Ports []GoogleCloudAiplatformV1Port
Immutable. List of ports to expose from the container. Vertex AI sends any prediction requests that it receives to the first port on this list. Vertex AI also sends liveness and health checks to this port. If you do not specify this field, it defaults to following value: json [ { "containerPort": 8080 } ] Vertex AI does not use ports other than the first one listed. This field corresponds to the ports field of the Kubernetes Containers v1 core API.
PredictRoute string
Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using projects.locations.endpoints.predict to this path on the container's IP address and port. Vertex AI then returns the container's response in the API response. For example, if you set this field to /foo, then when Vertex AI receives a prediction request, it forwards the request body in a POST request to the /foo path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
SharedMemorySizeMb string
Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes. TODO (b/306244185): Revise documentation before exposing.
StartupProbe GoogleCloudAiplatformV1Probe
Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.
imageUri This property is required. String
Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
args List<String>
Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD. Specify this field as an array of executable and arguments, similar to a Docker CMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the args field of the Kubernetes Containers v1 core API.
command List<String>
Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT. Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT's "exec" form, not its "shell" form. If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD, if either exists. If this field is not specified and the container does not have an ENTRYPOINT, then refer to the Docker documentation about how CMD and ENTRYPOINT interact. If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the command field of the Kubernetes Containers v1 core API.
deploymentTimeout String
Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
env List<GoogleCloudAiplatformV1EnvVar>
Immutable. List of environment variables to set in the container. After the container starts running, code running in the container can read these environment variables. Additionally, the command and args fields can reference these variables. Later entries in this list can also reference earlier entries. For example, the following example sets the variable VAR_2 to have the value foo bar: json [ { "name": "VAR_1", "value": "foo" }, { "name": "VAR_2", "value": "$(VAR_1) bar" } ] If you switch the order of the variables in the example, then the expansion does not occur. This field corresponds to the env field of the Kubernetes Containers v1 core API.
healthProbe GoogleCloudAiplatformV1Probe
Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
healthRoute String
Immutable. HTTP path on the container to send health checks to. Vertex AI intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about health checks. For example, if you set this field to /bar, then Vertex AI intermittently sends a GET request to the /bar path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/ DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
ports List<GoogleCloudAiplatformV1Port>
Immutable. List of ports to expose from the container. Vertex AI sends any prediction requests that it receives to the first port on this list. Vertex AI also sends liveness and health checks to this port. If you do not specify this field, it defaults to following value: json [ { "containerPort": 8080 } ] Vertex AI does not use ports other than the first one listed. This field corresponds to the ports field of the Kubernetes Containers v1 core API.
predictRoute String
Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using projects.locations.endpoints.predict to this path on the container's IP address and port. Vertex AI then returns the container's response in the API response. For example, if you set this field to /foo, then when Vertex AI receives a prediction request, it forwards the request body in a POST request to the /foo path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
sharedMemorySizeMb String
Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes. TODO (b/306244185): Revise documentation before exposing.
startupProbe GoogleCloudAiplatformV1Probe
Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.
imageUri This property is required. string
Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
args string[]
Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD. Specify this field as an array of executable and arguments, similar to a Docker CMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the args field of the Kubernetes Containers v1 core API.
command string[]
Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT. Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT's "exec" form, not its "shell" form. If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD, if either exists. If this field is not specified and the container does not have an ENTRYPOINT, then refer to the Docker documentation about how CMD and ENTRYPOINT interact. If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the command field of the Kubernetes Containers v1 core API.
deploymentTimeout string
Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
env GoogleCloudAiplatformV1EnvVar[]
Immutable. List of environment variables to set in the container. After the container starts running, code running in the container can read these environment variables. Additionally, the command and args fields can reference these variables. Later entries in this list can also reference earlier entries. For example, the following example sets the variable VAR_2 to have the value foo bar: json [ { "name": "VAR_1", "value": "foo" }, { "name": "VAR_2", "value": "$(VAR_1) bar" } ] If you switch the order of the variables in the example, then the expansion does not occur. This field corresponds to the env field of the Kubernetes Containers v1 core API.
healthProbe GoogleCloudAiplatformV1Probe
Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
healthRoute string
Immutable. HTTP path on the container to send health checks to. Vertex AI intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about health checks. For example, if you set this field to /bar, then Vertex AI intermittently sends a GET request to the /bar path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/ DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
ports GoogleCloudAiplatformV1Port[]
Immutable. List of ports to expose from the container. Vertex AI sends any prediction requests that it receives to the first port on this list. Vertex AI also sends liveness and health checks to this port. If you do not specify this field, it defaults to following value: json [ { "containerPort": 8080 } ] Vertex AI does not use ports other than the first one listed. This field corresponds to the ports field of the Kubernetes Containers v1 core API.
predictRoute string
Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using projects.locations.endpoints.predict to this path on the container's IP address and port. Vertex AI then returns the container's response in the API response. For example, if you set this field to /foo, then when Vertex AI receives a prediction request, it forwards the request body in a POST request to the /foo path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
sharedMemorySizeMb string
Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes. TODO (b/306244185): Revise documentation before exposing.
startupProbe GoogleCloudAiplatformV1Probe
Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.
image_uri This property is required. str
Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
args Sequence[str]
Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD. Specify this field as an array of executable and arguments, similar to a Docker CMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the args field of the Kubernetes Containers v1 core API.
command Sequence[str]
Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT. Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT's "exec" form, not its "shell" form. If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD, if either exists. If this field is not specified and the container does not have an ENTRYPOINT, then refer to the Docker documentation about how CMD and ENTRYPOINT interact. If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the command field of the Kubernetes Containers v1 core API.
deployment_timeout str
Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
env Sequence[GoogleCloudAiplatformV1EnvVar]
Immutable. List of environment variables to set in the container. After the container starts running, code running in the container can read these environment variables. Additionally, the command and args fields can reference these variables. Later entries in this list can also reference earlier entries. For example, the following example sets the variable VAR_2 to have the value foo bar: json [ { "name": "VAR_1", "value": "foo" }, { "name": "VAR_2", "value": "$(VAR_1) bar" } ] If you switch the order of the variables in the example, then the expansion does not occur. This field corresponds to the env field of the Kubernetes Containers v1 core API.
health_probe GoogleCloudAiplatformV1Probe
Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
health_route str
Immutable. HTTP path on the container to send health checks to. Vertex AI intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about health checks. For example, if you set this field to /bar, then Vertex AI intermittently sends a GET request to the /bar path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/ DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
ports Sequence[GoogleCloudAiplatformV1Port]
Immutable. List of ports to expose from the container. Vertex AI sends any prediction requests that it receives to the first port on this list. Vertex AI also sends liveness and health checks to this port. If you do not specify this field, it defaults to following value: json [ { "containerPort": 8080 } ] Vertex AI does not use ports other than the first one listed. This field corresponds to the ports field of the Kubernetes Containers v1 core API.
predict_route str
Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using projects.locations.endpoints.predict to this path on the container's IP address and port. Vertex AI then returns the container's response in the API response. For example, if you set this field to /foo, then when Vertex AI receives a prediction request, it forwards the request body in a POST request to the /foo path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
shared_memory_size_mb str
Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes. TODO (b/306244185): Revise documentation before exposing.
startup_probe GoogleCloudAiplatformV1Probe
Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.
imageUri This property is required. String
Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
args List<String>
Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD. Specify this field as an array of executable and arguments, similar to a Docker CMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the args field of the Kubernetes Containers v1 core API.
command List<String>
Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT. Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT's "exec" form, not its "shell" form. If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD, if either exists. If this field is not specified and the container does not have an ENTRYPOINT, then refer to the Docker documentation about how CMD and ENTRYPOINT interact. If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the command field of the Kubernetes Containers v1 core API.
deploymentTimeout String
Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
env List<Property Map>
Immutable. List of environment variables to set in the container. After the container starts running, code running in the container can read these environment variables. Additionally, the command and args fields can reference these variables. Later entries in this list can also reference earlier entries. For example, the following example sets the variable VAR_2 to have the value foo bar: json [ { "name": "VAR_1", "value": "foo" }, { "name": "VAR_2", "value": "$(VAR_1) bar" } ] If you switch the order of the variables in the example, then the expansion does not occur. This field corresponds to the env field of the Kubernetes Containers v1 core API.
healthProbe Property Map
Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
healthRoute String
Immutable. HTTP path on the container to send health checks to. Vertex AI intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about health checks. For example, if you set this field to /bar, then Vertex AI intermittently sends a GET request to the /bar path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/ DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
ports List<Property Map>
Immutable. List of ports to expose from the container. Vertex AI sends any prediction requests that it receives to the first port on this list. Vertex AI also sends liveness and health checks to this port. If you do not specify this field, it defaults to following value: json [ { "containerPort": 8080 } ] Vertex AI does not use ports other than the first one listed. This field corresponds to the ports field of the Kubernetes Containers v1 core API.
predictRoute String
Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using projects.locations.endpoints.predict to this path on the container's IP address and port. Vertex AI then returns the container's response in the API response. For example, if you set this field to /foo, then when Vertex AI receives a prediction request, it forwards the request body in a POST request to the /foo path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
sharedMemorySizeMb String
Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes. TODO (b/306244185): Revise documentation before exposing.
startupProbe Property Map
Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.

GoogleCloudAiplatformV1ModelContainerSpecResponse
, GoogleCloudAiplatformV1ModelContainerSpecResponseArgs

Args This property is required. List<string>
Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD. Specify this field as an array of executable and arguments, similar to a Docker CMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the args field of the Kubernetes Containers v1 core API.
Command This property is required. List<string>
Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT. Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT's "exec" form, not its "shell" form. If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD, if either exists. If this field is not specified and the container does not have an ENTRYPOINT, then refer to the Docker documentation about how CMD and ENTRYPOINT interact. If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the command field of the Kubernetes Containers v1 core API.
DeploymentTimeout This property is required. string
Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
Env This property is required. List<Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1EnvVarResponse>
Immutable. List of environment variables to set in the container. After the container starts running, code running in the container can read these environment variables. Additionally, the command and args fields can reference these variables. Later entries in this list can also reference earlier entries. For example, the following example sets the variable VAR_2 to have the value foo bar: json [ { "name": "VAR_1", "value": "foo" }, { "name": "VAR_2", "value": "$(VAR_1) bar" } ] If you switch the order of the variables in the example, then the expansion does not occur. This field corresponds to the env field of the Kubernetes Containers v1 core API.
HealthProbe This property is required. Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ProbeResponse
Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
HealthRoute This property is required. string
Immutable. HTTP path on the container to send health checks to. Vertex AI intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about health checks. For example, if you set this field to /bar, then Vertex AI intermittently sends a GET request to the /bar path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/ DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
ImageUri This property is required. string
Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
Ports This property is required. List<Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1PortResponse>
Immutable. List of ports to expose from the container. Vertex AI sends any prediction requests that it receives to the first port on this list. Vertex AI also sends liveness and health checks to this port. If you do not specify this field, it defaults to following value: json [ { "containerPort": 8080 } ] Vertex AI does not use ports other than the first one listed. This field corresponds to the ports field of the Kubernetes Containers v1 core API.
PredictRoute This property is required. string
Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using projects.locations.endpoints.predict to this path on the container's IP address and port. Vertex AI then returns the container's response in the API response. For example, if you set this field to /foo, then when Vertex AI receives a prediction request, it forwards the request body in a POST request to the /foo path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
SharedMemorySizeMb This property is required. string
Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes. TODO (b/306244185): Revise documentation before exposing.
StartupProbe This property is required. Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ProbeResponse
Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.
Args This property is required. []string
Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD. Specify this field as an array of executable and arguments, similar to a Docker CMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the args field of the Kubernetes Containers v1 core API.
Command This property is required. []string
Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT. Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT's "exec" form, not its "shell" form. If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD, if either exists. If this field is not specified and the container does not have an ENTRYPOINT, then refer to the Docker documentation about how CMD and ENTRYPOINT interact. If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the command field of the Kubernetes Containers v1 core API.
DeploymentTimeout This property is required. string
Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
Env This property is required. []GoogleCloudAiplatformV1EnvVarResponse
Immutable. List of environment variables to set in the container. After the container starts running, code running in the container can read these environment variables. Additionally, the command and args fields can reference these variables. Later entries in this list can also reference earlier entries. For example, the following example sets the variable VAR_2 to have the value foo bar: json [ { "name": "VAR_1", "value": "foo" }, { "name": "VAR_2", "value": "$(VAR_1) bar" } ] If you switch the order of the variables in the example, then the expansion does not occur. This field corresponds to the env field of the Kubernetes Containers v1 core API.
HealthProbe This property is required. GoogleCloudAiplatformV1ProbeResponse
Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
HealthRoute This property is required. string
Immutable. HTTP path on the container to send health checks to. Vertex AI intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about health checks. For example, if you set this field to /bar, then Vertex AI intermittently sends a GET request to the /bar path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/ DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
ImageUri This property is required. string
Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
Ports This property is required. []GoogleCloudAiplatformV1PortResponse
Immutable. List of ports to expose from the container. Vertex AI sends any prediction requests that it receives to the first port on this list. Vertex AI also sends liveness and health checks to this port. If you do not specify this field, it defaults to following value: json [ { "containerPort": 8080 } ] Vertex AI does not use ports other than the first one listed. This field corresponds to the ports field of the Kubernetes Containers v1 core API.
PredictRoute This property is required. string
Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using projects.locations.endpoints.predict to this path on the container's IP address and port. Vertex AI then returns the container's response in the API response. For example, if you set this field to /foo, then when Vertex AI receives a prediction request, it forwards the request body in a POST request to the /foo path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
SharedMemorySizeMb This property is required. string
Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes. TODO (b/306244185): Revise documentation before exposing.
StartupProbe This property is required. GoogleCloudAiplatformV1ProbeResponse
Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.
args This property is required. List<String>
Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD. Specify this field as an array of executable and arguments, similar to a Docker CMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the args field of the Kubernetes Containers v1 core API.
command This property is required. List<String>
Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT. Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT's "exec" form, not its "shell" form. If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD, if either exists. If this field is not specified and the container does not have an ENTRYPOINT, then refer to the Docker documentation about how CMD and ENTRYPOINT interact. If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the command field of the Kubernetes Containers v1 core API.
deploymentTimeout This property is required. String
Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
env This property is required. List<GoogleCloudAiplatformV1EnvVarResponse>
Immutable. List of environment variables to set in the container. After the container starts running, code running in the container can read these environment variables. Additionally, the command and args fields can reference these variables. Later entries in this list can also reference earlier entries. For example, the following example sets the variable VAR_2 to have the value foo bar: json [ { "name": "VAR_1", "value": "foo" }, { "name": "VAR_2", "value": "$(VAR_1) bar" } ] If you switch the order of the variables in the example, then the expansion does not occur. This field corresponds to the env field of the Kubernetes Containers v1 core API.
healthProbe This property is required. GoogleCloudAiplatformV1ProbeResponse
Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
healthRoute This property is required. String
Immutable. HTTP path on the container to send health checks to. Vertex AI intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about health checks. For example, if you set this field to /bar, then Vertex AI intermittently sends a GET request to the /bar path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/ DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
imageUri This property is required. String
Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
ports This property is required. List<GoogleCloudAiplatformV1PortResponse>
Immutable. List of ports to expose from the container. Vertex AI sends any prediction requests that it receives to the first port on this list. Vertex AI also sends liveness and health checks to this port. If you do not specify this field, it defaults to following value: json [ { "containerPort": 8080 } ] Vertex AI does not use ports other than the first one listed. This field corresponds to the ports field of the Kubernetes Containers v1 core API.
predictRoute This property is required. String
Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using projects.locations.endpoints.predict to this path on the container's IP address and port. Vertex AI then returns the container's response in the API response. For example, if you set this field to /foo, then when Vertex AI receives a prediction request, it forwards the request body in a POST request to the /foo path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
sharedMemorySizeMb This property is required. String
Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes. TODO (b/306244185): Revise documentation before exposing.
startupProbe This property is required. GoogleCloudAiplatformV1ProbeResponse
Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.
args This property is required. string[]
Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD. Specify this field as an array of executable and arguments, similar to a Docker CMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the args field of the Kubernetes Containers v1 core API.
command This property is required. string[]
Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT. Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT's "exec" form, not its "shell" form. If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD, if either exists. If this field is not specified and the container does not have an ENTRYPOINT, then refer to the Docker documentation about how CMD and ENTRYPOINT interact. If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the command field of the Kubernetes Containers v1 core API.
deploymentTimeout This property is required. string
Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
env This property is required. GoogleCloudAiplatformV1EnvVarResponse[]
Immutable. List of environment variables to set in the container. After the container starts running, code running in the container can read these environment variables. Additionally, the command and args fields can reference these variables. Later entries in this list can also reference earlier entries. For example, the following example sets the variable VAR_2 to have the value foo bar: json [ { "name": "VAR_1", "value": "foo" }, { "name": "VAR_2", "value": "$(VAR_1) bar" } ] If you switch the order of the variables in the example, then the expansion does not occur. This field corresponds to the env field of the Kubernetes Containers v1 core API.
healthProbe This property is required. GoogleCloudAiplatformV1ProbeResponse
Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
healthRoute This property is required. string
Immutable. HTTP path on the container to send health checks to. Vertex AI intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about health checks. For example, if you set this field to /bar, then Vertex AI intermittently sends a GET request to the /bar path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/ DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
imageUri This property is required. string
Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
ports This property is required. GoogleCloudAiplatformV1PortResponse[]
Immutable. List of ports to expose from the container. Vertex AI sends any prediction requests that it receives to the first port on this list. Vertex AI also sends liveness and health checks to this port. If you do not specify this field, it defaults to following value: json [ { "containerPort": 8080 } ] Vertex AI does not use ports other than the first one listed. This field corresponds to the ports field of the Kubernetes Containers v1 core API.
predictRoute This property is required. string
Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using projects.locations.endpoints.predict to this path on the container's IP address and port. Vertex AI then returns the container's response in the API response. For example, if you set this field to /foo, then when Vertex AI receives a prediction request, it forwards the request body in a POST request to the /foo path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
sharedMemorySizeMb This property is required. string
Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes. TODO (b/306244185): Revise documentation before exposing.
startupProbe This property is required. GoogleCloudAiplatformV1ProbeResponse
Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.
args This property is required. Sequence[str]
Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD. Specify this field as an array of executable and arguments, similar to a Docker CMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the args field of the Kubernetes Containers v1 core API.
command This property is required. Sequence[str]
Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT. Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT's "exec" form, not its "shell" form. If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD, if either exists. If this field is not specified and the container does not have an ENTRYPOINT, then refer to the Docker documentation about how CMD and ENTRYPOINT interact. If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the command field of the Kubernetes Containers v1 core API.
deployment_timeout This property is required. str
Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
env This property is required. Sequence[GoogleCloudAiplatformV1EnvVarResponse]
Immutable. List of environment variables to set in the container. After the container starts running, code running in the container can read these environment variables. Additionally, the command and args fields can reference these variables. Later entries in this list can also reference earlier entries. For example, the following example sets the variable VAR_2 to have the value foo bar: json [ { "name": "VAR_1", "value": "foo" }, { "name": "VAR_2", "value": "$(VAR_1) bar" } ] If you switch the order of the variables in the example, then the expansion does not occur. This field corresponds to the env field of the Kubernetes Containers v1 core API.
health_probe This property is required. GoogleCloudAiplatformV1ProbeResponse
Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
health_route This property is required. str
Immutable. HTTP path on the container to send health checks to. Vertex AI intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about health checks. For example, if you set this field to /bar, then Vertex AI intermittently sends a GET request to the /bar path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/ DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
image_uri This property is required. str
Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
ports This property is required. Sequence[GoogleCloudAiplatformV1PortResponse]
Immutable. List of ports to expose from the container. Vertex AI sends any prediction requests that it receives to the first port on this list. Vertex AI also sends liveness and health checks to this port. If you do not specify this field, it defaults to following value: json [ { "containerPort": 8080 } ] Vertex AI does not use ports other than the first one listed. This field corresponds to the ports field of the Kubernetes Containers v1 core API.
predict_route This property is required. str
Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using projects.locations.endpoints.predict to this path on the container's IP address and port. Vertex AI then returns the container's response in the API response. For example, if you set this field to /foo, then when Vertex AI receives a prediction request, it forwards the request body in a POST request to the /foo path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
shared_memory_size_mb This property is required. str
Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes. TODO (b/306244185): Revise documentation before exposing.
startup_probe This property is required. GoogleCloudAiplatformV1ProbeResponse
Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.
args This property is required. List<String>
Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD. Specify this field as an array of executable and arguments, similar to a Docker CMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the args field of the Kubernetes Containers v1 core API.
command This property is required. List<String>
Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT. Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT's "exec" form, not its "shell" form. If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD, if either exists. If this field is not specified and the container does not have an ENTRYPOINT, then refer to the Docker documentation about how CMD and ENTRYPOINT interact. If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the command field of the Kubernetes Containers v1 core API.
deploymentTimeout This property is required. String
Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
env This property is required. List<Property Map>
Immutable. List of environment variables to set in the container. After the container starts running, code running in the container can read these environment variables. Additionally, the command and args fields can reference these variables. Later entries in this list can also reference earlier entries. For example, the following example sets the variable VAR_2 to have the value foo bar: json [ { "name": "VAR_1", "value": "foo" }, { "name": "VAR_2", "value": "$(VAR_1) bar" } ] If you switch the order of the variables in the example, then the expansion does not occur. This field corresponds to the env field of the Kubernetes Containers v1 core API.
healthProbe This property is required. Property Map
Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
healthRoute This property is required. String
Immutable. HTTP path on the container to send health checks to. Vertex AI intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about health checks. For example, if you set this field to /bar, then Vertex AI intermittently sends a GET request to the /bar path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/ DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
imageUri This property is required. String
Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
ports This property is required. List<Property Map>
Immutable. List of ports to expose from the container. Vertex AI sends any prediction requests that it receives to the first port on this list. Vertex AI also sends liveness and health checks to this port. If you do not specify this field, it defaults to following value: json [ { "containerPort": 8080 } ] Vertex AI does not use ports other than the first one listed. This field corresponds to the ports field of the Kubernetes Containers v1 core API.
predictRoute This property is required. String
Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using projects.locations.endpoints.predict to this path on the container's IP address and port. Vertex AI then returns the container's response in the API response. For example, if you set this field to /foo, then when Vertex AI receives a prediction request, it forwards the request body in a POST request to the /foo path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
sharedMemorySizeMb This property is required. String
Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes. TODO (b/306244185): Revise documentation before exposing.
startupProbe This property is required. Property Map
Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.

GoogleCloudAiplatformV1Port
, GoogleCloudAiplatformV1PortArgs

ContainerPort int
The number of the port to expose on the pod's IP address. Must be a valid port number, between 1 and 65535 inclusive.
ContainerPort int
The number of the port to expose on the pod's IP address. Must be a valid port number, between 1 and 65535 inclusive.
containerPort Integer
The number of the port to expose on the pod's IP address. Must be a valid port number, between 1 and 65535 inclusive.
containerPort number
The number of the port to expose on the pod's IP address. Must be a valid port number, between 1 and 65535 inclusive.
container_port int
The number of the port to expose on the pod's IP address. Must be a valid port number, between 1 and 65535 inclusive.
containerPort Number
The number of the port to expose on the pod's IP address. Must be a valid port number, between 1 and 65535 inclusive.

GoogleCloudAiplatformV1PortResponse
, GoogleCloudAiplatformV1PortResponseArgs

ContainerPort This property is required. int
The number of the port to expose on the pod's IP address. Must be a valid port number, between 1 and 65535 inclusive.
ContainerPort This property is required. int
The number of the port to expose on the pod's IP address. Must be a valid port number, between 1 and 65535 inclusive.
containerPort This property is required. Integer
The number of the port to expose on the pod's IP address. Must be a valid port number, between 1 and 65535 inclusive.
containerPort This property is required. number
The number of the port to expose on the pod's IP address. Must be a valid port number, between 1 and 65535 inclusive.
container_port This property is required. int
The number of the port to expose on the pod's IP address. Must be a valid port number, between 1 and 65535 inclusive.
containerPort This property is required. Number
The number of the port to expose on the pod's IP address. Must be a valid port number, between 1 and 65535 inclusive.

GoogleCloudAiplatformV1PredictSchemata
, GoogleCloudAiplatformV1PredictSchemataArgs

InstanceSchemaUri string
Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used in PredictRequest.instances, ExplainRequest.instances and BatchPredictionJob.input_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
ParametersSchemaUri string
Immutable. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via PredictRequest.parameters, ExplainRequest.parameters and BatchPredictionJob.model_parameters. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no parameters are supported, then it is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
PredictionSchemaUri string
Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via PredictResponse.predictions, ExplainResponse.explanations, and BatchPredictionJob.output_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
InstanceSchemaUri string
Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used in PredictRequest.instances, ExplainRequest.instances and BatchPredictionJob.input_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
ParametersSchemaUri string
Immutable. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via PredictRequest.parameters, ExplainRequest.parameters and BatchPredictionJob.model_parameters. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no parameters are supported, then it is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
PredictionSchemaUri string
Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via PredictResponse.predictions, ExplainResponse.explanations, and BatchPredictionJob.output_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
instanceSchemaUri String
Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used in PredictRequest.instances, ExplainRequest.instances and BatchPredictionJob.input_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
parametersSchemaUri String
Immutable. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via PredictRequest.parameters, ExplainRequest.parameters and BatchPredictionJob.model_parameters. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no parameters are supported, then it is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
predictionSchemaUri String
Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via PredictResponse.predictions, ExplainResponse.explanations, and BatchPredictionJob.output_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
instanceSchemaUri string
Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used in PredictRequest.instances, ExplainRequest.instances and BatchPredictionJob.input_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
parametersSchemaUri string
Immutable. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via PredictRequest.parameters, ExplainRequest.parameters and BatchPredictionJob.model_parameters. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no parameters are supported, then it is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
predictionSchemaUri string
Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via PredictResponse.predictions, ExplainResponse.explanations, and BatchPredictionJob.output_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
instance_schema_uri str
Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used in PredictRequest.instances, ExplainRequest.instances and BatchPredictionJob.input_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
parameters_schema_uri str
Immutable. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via PredictRequest.parameters, ExplainRequest.parameters and BatchPredictionJob.model_parameters. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no parameters are supported, then it is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
prediction_schema_uri str
Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via PredictResponse.predictions, ExplainResponse.explanations, and BatchPredictionJob.output_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
instanceSchemaUri String
Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used in PredictRequest.instances, ExplainRequest.instances and BatchPredictionJob.input_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
parametersSchemaUri String
Immutable. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via PredictRequest.parameters, ExplainRequest.parameters and BatchPredictionJob.model_parameters. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no parameters are supported, then it is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
predictionSchemaUri String
Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via PredictResponse.predictions, ExplainResponse.explanations, and BatchPredictionJob.output_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.

GoogleCloudAiplatformV1PredictSchemataResponse
, GoogleCloudAiplatformV1PredictSchemataResponseArgs

InstanceSchemaUri This property is required. string
Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used in PredictRequest.instances, ExplainRequest.instances and BatchPredictionJob.input_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
ParametersSchemaUri This property is required. string
Immutable. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via PredictRequest.parameters, ExplainRequest.parameters and BatchPredictionJob.model_parameters. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no parameters are supported, then it is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
PredictionSchemaUri This property is required. string
Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via PredictResponse.predictions, ExplainResponse.explanations, and BatchPredictionJob.output_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
InstanceSchemaUri This property is required. string
Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used in PredictRequest.instances, ExplainRequest.instances and BatchPredictionJob.input_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
ParametersSchemaUri This property is required. string
Immutable. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via PredictRequest.parameters, ExplainRequest.parameters and BatchPredictionJob.model_parameters. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no parameters are supported, then it is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
PredictionSchemaUri This property is required. string
Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via PredictResponse.predictions, ExplainResponse.explanations, and BatchPredictionJob.output_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
instanceSchemaUri This property is required. String
Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used in PredictRequest.instances, ExplainRequest.instances and BatchPredictionJob.input_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
parametersSchemaUri This property is required. String
Immutable. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via PredictRequest.parameters, ExplainRequest.parameters and BatchPredictionJob.model_parameters. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no parameters are supported, then it is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
predictionSchemaUri This property is required. String
Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via PredictResponse.predictions, ExplainResponse.explanations, and BatchPredictionJob.output_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
instanceSchemaUri This property is required. string
Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used in PredictRequest.instances, ExplainRequest.instances and BatchPredictionJob.input_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
parametersSchemaUri This property is required. string
Immutable. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via PredictRequest.parameters, ExplainRequest.parameters and BatchPredictionJob.model_parameters. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no parameters are supported, then it is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
predictionSchemaUri This property is required. string
Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via PredictResponse.predictions, ExplainResponse.explanations, and BatchPredictionJob.output_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
instance_schema_uri This property is required. str
Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used in PredictRequest.instances, ExplainRequest.instances and BatchPredictionJob.input_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
parameters_schema_uri This property is required. str
Immutable. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via PredictRequest.parameters, ExplainRequest.parameters and BatchPredictionJob.model_parameters. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no parameters are supported, then it is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
prediction_schema_uri This property is required. str
Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via PredictResponse.predictions, ExplainResponse.explanations, and BatchPredictionJob.output_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
instanceSchemaUri This property is required. String
Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used in PredictRequest.instances, ExplainRequest.instances and BatchPredictionJob.input_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
parametersSchemaUri This property is required. String
Immutable. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via PredictRequest.parameters, ExplainRequest.parameters and BatchPredictionJob.model_parameters. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no parameters are supported, then it is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
predictionSchemaUri This property is required. String
Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via PredictResponse.predictions, ExplainResponse.explanations, and BatchPredictionJob.output_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.

GoogleCloudAiplatformV1Presets
, GoogleCloudAiplatformV1PresetsArgs

Modality Pulumi.GoogleNative.Aiplatform.V1.GoogleCloudAiplatformV1PresetsModality
The modality of the uploaded model, which automatically configures the distance measurement and feature normalization for the underlying example index and queries. If your model does not precisely fit one of these types, it is okay to choose the closest type.
Query Pulumi.GoogleNative.Aiplatform.V1.GoogleCloudAiplatformV1PresetsQuery
Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.
Modality GoogleCloudAiplatformV1PresetsModality
The modality of the uploaded model, which automatically configures the distance measurement and feature normalization for the underlying example index and queries. If your model does not precisely fit one of these types, it is okay to choose the closest type.
Query GoogleCloudAiplatformV1PresetsQuery
Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.
modality GoogleCloudAiplatformV1PresetsModality
The modality of the uploaded model, which automatically configures the distance measurement and feature normalization for the underlying example index and queries. If your model does not precisely fit one of these types, it is okay to choose the closest type.
query GoogleCloudAiplatformV1PresetsQuery
Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.
modality GoogleCloudAiplatformV1PresetsModality
The modality of the uploaded model, which automatically configures the distance measurement and feature normalization for the underlying example index and queries. If your model does not precisely fit one of these types, it is okay to choose the closest type.
query GoogleCloudAiplatformV1PresetsQuery
Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.
modality GoogleCloudAiplatformV1PresetsModality
The modality of the uploaded model, which automatically configures the distance measurement and feature normalization for the underlying example index and queries. If your model does not precisely fit one of these types, it is okay to choose the closest type.
query GoogleCloudAiplatformV1PresetsQuery
Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.
modality "MODALITY_UNSPECIFIED" | "IMAGE" | "TEXT" | "TABULAR"
The modality of the uploaded model, which automatically configures the distance measurement and feature normalization for the underlying example index and queries. If your model does not precisely fit one of these types, it is okay to choose the closest type.
query "PRECISE" | "FAST"
Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.

GoogleCloudAiplatformV1PresetsModality
, GoogleCloudAiplatformV1PresetsModalityArgs

ModalityUnspecified
MODALITY_UNSPECIFIEDShould not be set. Added as a recommended best practice for enums
Image
IMAGEIMAGE modality
Text
TEXTTEXT modality
Tabular
TABULARTABULAR modality
GoogleCloudAiplatformV1PresetsModalityModalityUnspecified
MODALITY_UNSPECIFIEDShould not be set. Added as a recommended best practice for enums
GoogleCloudAiplatformV1PresetsModalityImage
IMAGEIMAGE modality
GoogleCloudAiplatformV1PresetsModalityText
TEXTTEXT modality
GoogleCloudAiplatformV1PresetsModalityTabular
TABULARTABULAR modality
ModalityUnspecified
MODALITY_UNSPECIFIEDShould not be set. Added as a recommended best practice for enums
Image
IMAGEIMAGE modality
Text
TEXTTEXT modality
Tabular
TABULARTABULAR modality
ModalityUnspecified
MODALITY_UNSPECIFIEDShould not be set. Added as a recommended best practice for enums
Image
IMAGEIMAGE modality
Text
TEXTTEXT modality
Tabular
TABULARTABULAR modality
MODALITY_UNSPECIFIED
MODALITY_UNSPECIFIEDShould not be set. Added as a recommended best practice for enums
IMAGE
IMAGEIMAGE modality
TEXT
TEXTTEXT modality
TABULAR
TABULARTABULAR modality
"MODALITY_UNSPECIFIED"
MODALITY_UNSPECIFIEDShould not be set. Added as a recommended best practice for enums
"IMAGE"
IMAGEIMAGE modality
"TEXT"
TEXTTEXT modality
"TABULAR"
TABULARTABULAR modality

GoogleCloudAiplatformV1PresetsQuery
, GoogleCloudAiplatformV1PresetsQueryArgs

Precise
PRECISEMore precise neighbors as a trade-off against slower response.
Fast
FASTFaster response as a trade-off against less precise neighbors.
GoogleCloudAiplatformV1PresetsQueryPrecise
PRECISEMore precise neighbors as a trade-off against slower response.
GoogleCloudAiplatformV1PresetsQueryFast
FASTFaster response as a trade-off against less precise neighbors.
Precise
PRECISEMore precise neighbors as a trade-off against slower response.
Fast
FASTFaster response as a trade-off against less precise neighbors.
Precise
PRECISEMore precise neighbors as a trade-off against slower response.
Fast
FASTFaster response as a trade-off against less precise neighbors.
PRECISE
PRECISEMore precise neighbors as a trade-off against slower response.
FAST
FASTFaster response as a trade-off against less precise neighbors.
"PRECISE"
PRECISEMore precise neighbors as a trade-off against slower response.
"FAST"
FASTFaster response as a trade-off against less precise neighbors.

GoogleCloudAiplatformV1PresetsResponse
, GoogleCloudAiplatformV1PresetsResponseArgs

Modality This property is required. string
The modality of the uploaded model, which automatically configures the distance measurement and feature normalization for the underlying example index and queries. If your model does not precisely fit one of these types, it is okay to choose the closest type.
Query This property is required. string
Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.
Modality This property is required. string
The modality of the uploaded model, which automatically configures the distance measurement and feature normalization for the underlying example index and queries. If your model does not precisely fit one of these types, it is okay to choose the closest type.
Query This property is required. string
Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.
modality This property is required. String
The modality of the uploaded model, which automatically configures the distance measurement and feature normalization for the underlying example index and queries. If your model does not precisely fit one of these types, it is okay to choose the closest type.
query This property is required. String
Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.
modality This property is required. string
The modality of the uploaded model, which automatically configures the distance measurement and feature normalization for the underlying example index and queries. If your model does not precisely fit one of these types, it is okay to choose the closest type.
query This property is required. string
Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.
modality This property is required. str
The modality of the uploaded model, which automatically configures the distance measurement and feature normalization for the underlying example index and queries. If your model does not precisely fit one of these types, it is okay to choose the closest type.
query This property is required. str
Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.
modality This property is required. String
The modality of the uploaded model, which automatically configures the distance measurement and feature normalization for the underlying example index and queries. If your model does not precisely fit one of these types, it is okay to choose the closest type.
query This property is required. String
Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.

GoogleCloudAiplatformV1Probe
, GoogleCloudAiplatformV1ProbeArgs

Exec Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ProbeExecAction
Exec specifies the action to take.
PeriodSeconds int
How often (in seconds) to perform the probe. Default to 10 seconds. Minimum value is 1. Must be less than timeout_seconds. Maps to Kubernetes probe argument 'periodSeconds'.
TimeoutSeconds int
Number of seconds after which the probe times out. Defaults to 1 second. Minimum value is 1. Must be greater or equal to period_seconds. Maps to Kubernetes probe argument 'timeoutSeconds'.
Exec GoogleCloudAiplatformV1ProbeExecAction
Exec specifies the action to take.
PeriodSeconds int
How often (in seconds) to perform the probe. Default to 10 seconds. Minimum value is 1. Must be less than timeout_seconds. Maps to Kubernetes probe argument 'periodSeconds'.
TimeoutSeconds int
Number of seconds after which the probe times out. Defaults to 1 second. Minimum value is 1. Must be greater or equal to period_seconds. Maps to Kubernetes probe argument 'timeoutSeconds'.
exec GoogleCloudAiplatformV1ProbeExecAction
Exec specifies the action to take.
periodSeconds Integer
How often (in seconds) to perform the probe. Default to 10 seconds. Minimum value is 1. Must be less than timeout_seconds. Maps to Kubernetes probe argument 'periodSeconds'.
timeoutSeconds Integer
Number of seconds after which the probe times out. Defaults to 1 second. Minimum value is 1. Must be greater or equal to period_seconds. Maps to Kubernetes probe argument 'timeoutSeconds'.
exec GoogleCloudAiplatformV1ProbeExecAction
Exec specifies the action to take.
periodSeconds number
How often (in seconds) to perform the probe. Default to 10 seconds. Minimum value is 1. Must be less than timeout_seconds. Maps to Kubernetes probe argument 'periodSeconds'.
timeoutSeconds number
Number of seconds after which the probe times out. Defaults to 1 second. Minimum value is 1. Must be greater or equal to period_seconds. Maps to Kubernetes probe argument 'timeoutSeconds'.
exec_ GoogleCloudAiplatformV1ProbeExecAction
Exec specifies the action to take.
period_seconds int
How often (in seconds) to perform the probe. Default to 10 seconds. Minimum value is 1. Must be less than timeout_seconds. Maps to Kubernetes probe argument 'periodSeconds'.
timeout_seconds int
Number of seconds after which the probe times out. Defaults to 1 second. Minimum value is 1. Must be greater or equal to period_seconds. Maps to Kubernetes probe argument 'timeoutSeconds'.
exec Property Map
Exec specifies the action to take.
periodSeconds Number
How often (in seconds) to perform the probe. Default to 10 seconds. Minimum value is 1. Must be less than timeout_seconds. Maps to Kubernetes probe argument 'periodSeconds'.
timeoutSeconds Number
Number of seconds after which the probe times out. Defaults to 1 second. Minimum value is 1. Must be greater or equal to period_seconds. Maps to Kubernetes probe argument 'timeoutSeconds'.

GoogleCloudAiplatformV1ProbeExecAction
, GoogleCloudAiplatformV1ProbeExecActionArgs

Command List<string>
Command is the command line to execute inside the container, the working directory for the command is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.
Command []string
Command is the command line to execute inside the container, the working directory for the command is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.
command List<String>
Command is the command line to execute inside the container, the working directory for the command is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.
command string[]
Command is the command line to execute inside the container, the working directory for the command is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.
command Sequence[str]
Command is the command line to execute inside the container, the working directory for the command is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.
command List<String>
Command is the command line to execute inside the container, the working directory for the command is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.

GoogleCloudAiplatformV1ProbeExecActionResponse
, GoogleCloudAiplatformV1ProbeExecActionResponseArgs

Command This property is required. List<string>
Command is the command line to execute inside the container, the working directory for the command is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.
Command This property is required. []string
Command is the command line to execute inside the container, the working directory for the command is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.
command This property is required. List<String>
Command is the command line to execute inside the container, the working directory for the command is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.
command This property is required. string[]
Command is the command line to execute inside the container, the working directory for the command is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.
command This property is required. Sequence[str]
Command is the command line to execute inside the container, the working directory for the command is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.
command This property is required. List<String>
Command is the command line to execute inside the container, the working directory for the command is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.

GoogleCloudAiplatformV1ProbeResponse
, GoogleCloudAiplatformV1ProbeResponseArgs

Exec This property is required. Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ProbeExecActionResponse
Exec specifies the action to take.
PeriodSeconds This property is required. int
How often (in seconds) to perform the probe. Default to 10 seconds. Minimum value is 1. Must be less than timeout_seconds. Maps to Kubernetes probe argument 'periodSeconds'.
TimeoutSeconds This property is required. int
Number of seconds after which the probe times out. Defaults to 1 second. Minimum value is 1. Must be greater or equal to period_seconds. Maps to Kubernetes probe argument 'timeoutSeconds'.
Exec This property is required. GoogleCloudAiplatformV1ProbeExecActionResponse
Exec specifies the action to take.
PeriodSeconds This property is required. int
How often (in seconds) to perform the probe. Default to 10 seconds. Minimum value is 1. Must be less than timeout_seconds. Maps to Kubernetes probe argument 'periodSeconds'.
TimeoutSeconds This property is required. int
Number of seconds after which the probe times out. Defaults to 1 second. Minimum value is 1. Must be greater or equal to period_seconds. Maps to Kubernetes probe argument 'timeoutSeconds'.
exec This property is required. GoogleCloudAiplatformV1ProbeExecActionResponse
Exec specifies the action to take.
periodSeconds This property is required. Integer
How often (in seconds) to perform the probe. Default to 10 seconds. Minimum value is 1. Must be less than timeout_seconds. Maps to Kubernetes probe argument 'periodSeconds'.
timeoutSeconds This property is required. Integer
Number of seconds after which the probe times out. Defaults to 1 second. Minimum value is 1. Must be greater or equal to period_seconds. Maps to Kubernetes probe argument 'timeoutSeconds'.
exec This property is required. GoogleCloudAiplatformV1ProbeExecActionResponse
Exec specifies the action to take.
periodSeconds This property is required. number
How often (in seconds) to perform the probe. Default to 10 seconds. Minimum value is 1. Must be less than timeout_seconds. Maps to Kubernetes probe argument 'periodSeconds'.
timeoutSeconds This property is required. number
Number of seconds after which the probe times out. Defaults to 1 second. Minimum value is 1. Must be greater or equal to period_seconds. Maps to Kubernetes probe argument 'timeoutSeconds'.
exec_ This property is required. GoogleCloudAiplatformV1ProbeExecActionResponse
Exec specifies the action to take.
period_seconds This property is required. int
How often (in seconds) to perform the probe. Default to 10 seconds. Minimum value is 1. Must be less than timeout_seconds. Maps to Kubernetes probe argument 'periodSeconds'.
timeout_seconds This property is required. int
Number of seconds after which the probe times out. Defaults to 1 second. Minimum value is 1. Must be greater or equal to period_seconds. Maps to Kubernetes probe argument 'timeoutSeconds'.
exec This property is required. Property Map
Exec specifies the action to take.
periodSeconds This property is required. Number
How often (in seconds) to perform the probe. Default to 10 seconds. Minimum value is 1. Must be less than timeout_seconds. Maps to Kubernetes probe argument 'periodSeconds'.
timeoutSeconds This property is required. Number
Number of seconds after which the probe times out. Defaults to 1 second. Minimum value is 1. Must be greater or equal to period_seconds. Maps to Kubernetes probe argument 'timeoutSeconds'.

GoogleCloudAiplatformV1ResourcesConsumedResponse
, GoogleCloudAiplatformV1ResourcesConsumedResponseArgs

ReplicaHours This property is required. double
The number of replica hours used. Note that many replicas may run in parallel, and additionally any given work may be queued for some time. Therefore this value is not strictly related to wall time.
ReplicaHours This property is required. float64
The number of replica hours used. Note that many replicas may run in parallel, and additionally any given work may be queued for some time. Therefore this value is not strictly related to wall time.
replicaHours This property is required. Double
The number of replica hours used. Note that many replicas may run in parallel, and additionally any given work may be queued for some time. Therefore this value is not strictly related to wall time.
replicaHours This property is required. number
The number of replica hours used. Note that many replicas may run in parallel, and additionally any given work may be queued for some time. Therefore this value is not strictly related to wall time.
replica_hours This property is required. float
The number of replica hours used. Note that many replicas may run in parallel, and additionally any given work may be queued for some time. Therefore this value is not strictly related to wall time.
replicaHours This property is required. Number
The number of replica hours used. Note that many replicas may run in parallel, and additionally any given work may be queued for some time. Therefore this value is not strictly related to wall time.

GoogleCloudAiplatformV1SampledShapleyAttribution
, GoogleCloudAiplatformV1SampledShapleyAttributionArgs

PathCount This property is required. int
The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.
PathCount This property is required. int
The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.
pathCount This property is required. Integer
The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.
pathCount This property is required. number
The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.
path_count This property is required. int
The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.
pathCount This property is required. Number
The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.

GoogleCloudAiplatformV1SampledShapleyAttributionResponse
, GoogleCloudAiplatformV1SampledShapleyAttributionResponseArgs

PathCount This property is required. int
The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.
PathCount This property is required. int
The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.
pathCount This property is required. Integer
The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.
pathCount This property is required. number
The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.
path_count This property is required. int
The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.
pathCount This property is required. Number
The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.

GoogleCloudAiplatformV1SmoothGradConfig
, GoogleCloudAiplatformV1SmoothGradConfigArgs

FeatureNoiseSigma Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1FeatureNoiseSigma
This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.
NoiseSigma double
This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about normalization. For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
NoisySampleCount int
The number of gradient samples to use for approximation. The higher this number, the more accurate the gradient is, but the runtime complexity increases by this factor as well. Valid range of its value is [1, 50]. Defaults to 3.
FeatureNoiseSigma GoogleCloudAiplatformV1FeatureNoiseSigma
This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.
NoiseSigma float64
This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about normalization. For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
NoisySampleCount int
The number of gradient samples to use for approximation. The higher this number, the more accurate the gradient is, but the runtime complexity increases by this factor as well. Valid range of its value is [1, 50]. Defaults to 3.
featureNoiseSigma GoogleCloudAiplatformV1FeatureNoiseSigma
This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.
noiseSigma Double
This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about normalization. For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
noisySampleCount Integer
The number of gradient samples to use for approximation. The higher this number, the more accurate the gradient is, but the runtime complexity increases by this factor as well. Valid range of its value is [1, 50]. Defaults to 3.
featureNoiseSigma GoogleCloudAiplatformV1FeatureNoiseSigma
This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.
noiseSigma number
This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about normalization. For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
noisySampleCount number
The number of gradient samples to use for approximation. The higher this number, the more accurate the gradient is, but the runtime complexity increases by this factor as well. Valid range of its value is [1, 50]. Defaults to 3.
feature_noise_sigma GoogleCloudAiplatformV1FeatureNoiseSigma
This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.
noise_sigma float
This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about normalization. For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
noisy_sample_count int
The number of gradient samples to use for approximation. The higher this number, the more accurate the gradient is, but the runtime complexity increases by this factor as well. Valid range of its value is [1, 50]. Defaults to 3.
featureNoiseSigma Property Map
This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.
noiseSigma Number
This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about normalization. For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
noisySampleCount Number
The number of gradient samples to use for approximation. The higher this number, the more accurate the gradient is, but the runtime complexity increases by this factor as well. Valid range of its value is [1, 50]. Defaults to 3.

GoogleCloudAiplatformV1SmoothGradConfigResponse
, GoogleCloudAiplatformV1SmoothGradConfigResponseArgs

FeatureNoiseSigma This property is required. Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1FeatureNoiseSigmaResponse
This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.
NoiseSigma This property is required. double
This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about normalization. For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
NoisySampleCount This property is required. int
The number of gradient samples to use for approximation. The higher this number, the more accurate the gradient is, but the runtime complexity increases by this factor as well. Valid range of its value is [1, 50]. Defaults to 3.
FeatureNoiseSigma This property is required. GoogleCloudAiplatformV1FeatureNoiseSigmaResponse
This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.
NoiseSigma This property is required. float64
This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about normalization. For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
NoisySampleCount This property is required. int
The number of gradient samples to use for approximation. The higher this number, the more accurate the gradient is, but the runtime complexity increases by this factor as well. Valid range of its value is [1, 50]. Defaults to 3.
featureNoiseSigma This property is required. GoogleCloudAiplatformV1FeatureNoiseSigmaResponse
This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.
noiseSigma This property is required. Double
This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about normalization. For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
noisySampleCount This property is required. Integer
The number of gradient samples to use for approximation. The higher this number, the more accurate the gradient is, but the runtime complexity increases by this factor as well. Valid range of its value is [1, 50]. Defaults to 3.
featureNoiseSigma This property is required. GoogleCloudAiplatformV1FeatureNoiseSigmaResponse
This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.
noiseSigma This property is required. number
This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about normalization. For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
noisySampleCount This property is required. number
The number of gradient samples to use for approximation. The higher this number, the more accurate the gradient is, but the runtime complexity increases by this factor as well. Valid range of its value is [1, 50]. Defaults to 3.
feature_noise_sigma This property is required. GoogleCloudAiplatformV1FeatureNoiseSigmaResponse
This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.
noise_sigma This property is required. float
This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about normalization. For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
noisy_sample_count This property is required. int
The number of gradient samples to use for approximation. The higher this number, the more accurate the gradient is, but the runtime complexity increases by this factor as well. Valid range of its value is [1, 50]. Defaults to 3.
featureNoiseSigma This property is required. Property Map
This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.
noiseSigma This property is required. Number
This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about normalization. For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
noisySampleCount This property is required. Number
The number of gradient samples to use for approximation. The higher this number, the more accurate the gradient is, but the runtime complexity increases by this factor as well. Valid range of its value is [1, 50]. Defaults to 3.

GoogleCloudAiplatformV1UnmanagedContainerModel
, GoogleCloudAiplatformV1UnmanagedContainerModelArgs

ArtifactUri string
The path to the directory containing the Model artifact and any of its supporting files.
ContainerSpec Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ModelContainerSpec
Input only. The specification of the container that is to be used when deploying this Model.
PredictSchemata Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1PredictSchemata
Contains the schemata used in Model's predictions and explanations
ArtifactUri string
The path to the directory containing the Model artifact and any of its supporting files.
ContainerSpec GoogleCloudAiplatformV1ModelContainerSpec
Input only. The specification of the container that is to be used when deploying this Model.
PredictSchemata GoogleCloudAiplatformV1PredictSchemata
Contains the schemata used in Model's predictions and explanations
artifactUri String
The path to the directory containing the Model artifact and any of its supporting files.
containerSpec GoogleCloudAiplatformV1ModelContainerSpec
Input only. The specification of the container that is to be used when deploying this Model.
predictSchemata GoogleCloudAiplatformV1PredictSchemata
Contains the schemata used in Model's predictions and explanations
artifactUri string
The path to the directory containing the Model artifact and any of its supporting files.
containerSpec GoogleCloudAiplatformV1ModelContainerSpec
Input only. The specification of the container that is to be used when deploying this Model.
predictSchemata GoogleCloudAiplatformV1PredictSchemata
Contains the schemata used in Model's predictions and explanations
artifact_uri str
The path to the directory containing the Model artifact and any of its supporting files.
container_spec GoogleCloudAiplatformV1ModelContainerSpec
Input only. The specification of the container that is to be used when deploying this Model.
predict_schemata GoogleCloudAiplatformV1PredictSchemata
Contains the schemata used in Model's predictions and explanations
artifactUri String
The path to the directory containing the Model artifact and any of its supporting files.
containerSpec Property Map
Input only. The specification of the container that is to be used when deploying this Model.
predictSchemata Property Map
Contains the schemata used in Model's predictions and explanations

GoogleCloudAiplatformV1UnmanagedContainerModelResponse
, GoogleCloudAiplatformV1UnmanagedContainerModelResponseArgs

ArtifactUri This property is required. string
The path to the directory containing the Model artifact and any of its supporting files.
ContainerSpec This property is required. Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ModelContainerSpecResponse
Input only. The specification of the container that is to be used when deploying this Model.
PredictSchemata This property is required. Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1PredictSchemataResponse
Contains the schemata used in Model's predictions and explanations
ArtifactUri This property is required. string
The path to the directory containing the Model artifact and any of its supporting files.
ContainerSpec This property is required. GoogleCloudAiplatformV1ModelContainerSpecResponse
Input only. The specification of the container that is to be used when deploying this Model.
PredictSchemata This property is required. GoogleCloudAiplatformV1PredictSchemataResponse
Contains the schemata used in Model's predictions and explanations
artifactUri This property is required. String
The path to the directory containing the Model artifact and any of its supporting files.
containerSpec This property is required. GoogleCloudAiplatformV1ModelContainerSpecResponse
Input only. The specification of the container that is to be used when deploying this Model.
predictSchemata This property is required. GoogleCloudAiplatformV1PredictSchemataResponse
Contains the schemata used in Model's predictions and explanations
artifactUri This property is required. string
The path to the directory containing the Model artifact and any of its supporting files.
containerSpec This property is required. GoogleCloudAiplatformV1ModelContainerSpecResponse
Input only. The specification of the container that is to be used when deploying this Model.
predictSchemata This property is required. GoogleCloudAiplatformV1PredictSchemataResponse
Contains the schemata used in Model's predictions and explanations
artifact_uri This property is required. str
The path to the directory containing the Model artifact and any of its supporting files.
container_spec This property is required. GoogleCloudAiplatformV1ModelContainerSpecResponse
Input only. The specification of the container that is to be used when deploying this Model.
predict_schemata This property is required. GoogleCloudAiplatformV1PredictSchemataResponse
Contains the schemata used in Model's predictions and explanations
artifactUri This property is required. String
The path to the directory containing the Model artifact and any of its supporting files.
containerSpec This property is required. Property Map
Input only. The specification of the container that is to be used when deploying this Model.
predictSchemata This property is required. Property Map
Contains the schemata used in Model's predictions and explanations

GoogleCloudAiplatformV1XraiAttribution
, GoogleCloudAiplatformV1XraiAttributionArgs

StepCount This property is required. int
The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.
BlurBaselineConfig Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1BlurBaselineConfig
Config for XRAI with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
SmoothGradConfig Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1SmoothGradConfig
Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
StepCount This property is required. int
The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.
BlurBaselineConfig GoogleCloudAiplatformV1BlurBaselineConfig
Config for XRAI with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
SmoothGradConfig GoogleCloudAiplatformV1SmoothGradConfig
Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
stepCount This property is required. Integer
The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.
blurBaselineConfig GoogleCloudAiplatformV1BlurBaselineConfig
Config for XRAI with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
smoothGradConfig GoogleCloudAiplatformV1SmoothGradConfig
Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
stepCount This property is required. number
The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.
blurBaselineConfig GoogleCloudAiplatformV1BlurBaselineConfig
Config for XRAI with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
smoothGradConfig GoogleCloudAiplatformV1SmoothGradConfig
Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
step_count This property is required. int
The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.
blur_baseline_config GoogleCloudAiplatformV1BlurBaselineConfig
Config for XRAI with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
smooth_grad_config GoogleCloudAiplatformV1SmoothGradConfig
Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
stepCount This property is required. Number
The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.
blurBaselineConfig Property Map
Config for XRAI with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
smoothGradConfig Property Map
Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf

GoogleCloudAiplatformV1XraiAttributionResponse
, GoogleCloudAiplatformV1XraiAttributionResponseArgs

BlurBaselineConfig This property is required. Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1BlurBaselineConfigResponse
Config for XRAI with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
SmoothGradConfig This property is required. Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1SmoothGradConfigResponse
Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
StepCount This property is required. int
The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.
BlurBaselineConfig This property is required. GoogleCloudAiplatformV1BlurBaselineConfigResponse
Config for XRAI with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
SmoothGradConfig This property is required. GoogleCloudAiplatformV1SmoothGradConfigResponse
Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
StepCount This property is required. int
The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.
blurBaselineConfig This property is required. GoogleCloudAiplatformV1BlurBaselineConfigResponse
Config for XRAI with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
smoothGradConfig This property is required. GoogleCloudAiplatformV1SmoothGradConfigResponse
Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
stepCount This property is required. Integer
The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.
blurBaselineConfig This property is required. GoogleCloudAiplatformV1BlurBaselineConfigResponse
Config for XRAI with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
smoothGradConfig This property is required. GoogleCloudAiplatformV1SmoothGradConfigResponse
Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
stepCount This property is required. number
The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.
blur_baseline_config This property is required. GoogleCloudAiplatformV1BlurBaselineConfigResponse
Config for XRAI with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
smooth_grad_config This property is required. GoogleCloudAiplatformV1SmoothGradConfigResponse
Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
step_count This property is required. int
The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.
blurBaselineConfig This property is required. Property Map
Config for XRAI with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
smoothGradConfig This property is required. Property Map
Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
stepCount This property is required. Number
The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.

GoogleRpcStatusResponse
, GoogleRpcStatusResponseArgs

Code This property is required. int
The status code, which should be an enum value of google.rpc.Code.
Details This property is required. List<ImmutableDictionary<string, string>>
A list of messages that carry the error details. There is a common set of message types for APIs to use.
Message This property is required. string
A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
Code This property is required. int
The status code, which should be an enum value of google.rpc.Code.
Details This property is required. []map[string]string
A list of messages that carry the error details. There is a common set of message types for APIs to use.
Message This property is required. string
A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
code This property is required. Integer
The status code, which should be an enum value of google.rpc.Code.
details This property is required. List<Map<String,String>>
A list of messages that carry the error details. There is a common set of message types for APIs to use.
message This property is required. String
A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
code This property is required. number
The status code, which should be an enum value of google.rpc.Code.
details This property is required. {[key: string]: string}[]
A list of messages that carry the error details. There is a common set of message types for APIs to use.
message This property is required. string
A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
code This property is required. int
The status code, which should be an enum value of google.rpc.Code.
details This property is required. Sequence[Mapping[str, str]]
A list of messages that carry the error details. There is a common set of message types for APIs to use.
message This property is required. str
A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
code This property is required. Number
The status code, which should be an enum value of google.rpc.Code.
details This property is required. List<Map<String>>
A list of messages that carry the error details. There is a common set of message types for APIs to use.
message This property is required. String
A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.

Package Details

Repository
Google Cloud Native pulumi/pulumi-google-native
License
Apache-2.0

Google Cloud Native is in preview. Google Cloud Classic is fully supported.

Google Cloud Native v0.32.0 published on Wednesday, Nov 29, 2023 by Pulumi
pulumi/pulumi-google-native